In this video, I get into diffusion models and specifically we look into denoising diffusion probabilistic models (DDPM). I try to provide a comprehensive guide to understanding entire maths behind it and training diffusion models ( denoising diffusion probabilistic models ).
🔍 Video Highlights:
1. Overview of Diffusion Models: We first look at the code idea in diffusion models
2. DDPM Demystified: We break down entire math in Denoising Diffusion Probabilistic Models in order to gain a deep understanding of the algorithms driving these innovative models.
3. Training and Sampling in Diffusion Models: Finally we look step-by-step on how these are trained and how one can sample images in Denoising Diffusion Probabilistic Models
Timestamps
00:00 Introduction
00:25 Basic Idea of Diffusion Models
02:23 Why call this Diffusion Models
05:24 Transition function in Denoising Diffusion Probabilistic Models - DDPM
07:28 Distribution at end of forward Diffusion Process
10:17 Noise Schedule in Diffusion Models
11:36 Recursion to get from original image to noisy image
13:40 Reverse Process in Diffusion Models
14:40 Variational Lower Bound in Denoising Diffusion Probabilistic Models - DDPM
17:02 Simplifying the Likelihood for Diffusion Models
19:08 Ground Truth Denoising Distribution
22:31 Loss as Original Image Prediction
24:10 Loss as Noise Prediction
26:26 Training of DDPM - Denoising Diffusion Probabilistic Models
27:17 Sampling in DDPM - Denoising Diffusion Probabilistic Models
28:30 Why create this video on Diffusion Models
29:10 Thank You
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Useful Resources
Paper Link - [ Ссылка ]
Jeremy Howard - [ Ссылка ]
Calvin Luo - [ Ссылка ]
Joseph Rocca - [ Ссылка ]
Outlier - [ Ссылка ]
Lilian Weng - [ Ссылка ]
Ayan Das - [ Ссылка ]
Jonathan Goodman - [ Ссылка ]
📌 Keywords:
#DiffusionModels #DDPMExplained
Background Track - Fruits of Life by Jimena Contreras
Email - explainingai.official@gmail.com
