Stable Diffusion from Scratch in PyTorch | Unconditional Latent Diffusion Models

About this lesson

In this video, we'll cover everything from the building blocks of stable diffusion to its implementation in PyTorch and see how to build and train Stable Diffusion from scratch. This is Part I of the tutorial where I explain latent diffusion models specifically unconditional latent diffusion models. We dive deep into what is latent diffusion , how latent diffusion works , what are the components and losses used in training latent diffusion models and then finally implement and train latent diffusion models. The second part will cover conditional latent diffusion models and we will transition to Stable diffusion. The series will be a stable diffusion guide from scratch and you will be able to code stable diffusion in pytorch by yourself by end of it. ⏱️ Timestamps 00:00 Intro 00:56 Recap of Diffusion Models 3:31 Why Latent Diffusion Models 4:31 Introduction to Latent Diffusion Models 5:55 Review of VAE : Variational Auto Encoder 6:29 Review of VQVAE : Vector Quantised Variational Auto Encoder 7:36 Issue with L2 Reconstruction Loss for Latent Diffusion Models 8:50 Perceptual Loss 13:44 LPIPS Implementation 16:40 Adversarial Loss in Latent Diffusion Models 19:38 AutoEncoder Architecture in Latent Diffusion Models 23:04 VAE Implementation vs VQVAE Implementation 24:22 Autoencoder Implementation for Latent Diffusion Models 32:23 Training AutoEncoder for Latent Diffusion Models 32:37 Discriminator for Latent Diffusion Models 36:33 Results of Autoencoder Training 37:40 VQGAN = VQVAE + LPIPS + Discriminator 38:26 Latent Diffusion Model Architecture 39:37 Training Diffusion of Latent Diffusion Models 41:11 Latent Diffusion Model Results 41:45 Whats Next 42:18 Outro Paper - http://tinyurl.com/exai-latent-diffusion-paper Implementation - http://tinyurl.com/exai-stable-diffusion-repo 🔔 Subscribe : https://tinyurl.com/exai-channel-link 📌 Keywords: #stablediffusion #stable_diffusion

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