Denoising Diffusion Probabilistic Models | DDPM Explained

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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📌 Keywords:
#DiffusionModels #DDPMExplained

Background Track - Fruits of Life by Jimena Contreras

[lucamautino]

Amazing job, I'm studyinh DDPMs for my thesis and this is the best resource you can find by far!

[HassanHamidi-vs]

Definitely the best explanation I've ever seen on this topic. Keep it up! :)

[raghavamorusupalli]

I learnt maths of DM from this lecture. Thank you

[amirzarei]

without a doubt the best video ever made on the subject of DDPM. Even better than the original paper. Thank you very much for that. ❤

[sladewinter]

Thanks man, this really helped clear some fundamental doubts which remained even after going through multiple articles on DDPMs. Terrific job!

[shizhouhuang]

That is the best video that i have watched about teaching the diffusion model.

[bayesianmonk]

I watched your video again, and cannot give you enough compliments on it! Great job!

[xichen]

VERY VERY GREAT video! Helps a lot for understanding why things are done in the ways presented in the original paper. Thank you so much!!!

[raulable]

Wow! This is an incredibly clear explanation of the complex mathematics behind DDPM. Thank you so much, Tushar! This video is a real gem. The formulas may seem intimidating at first, but it's amazing how such a complex model can be derived from a fundamentally simple idea.

[daryoushmehrtash]

Thanks. Many interesting nuggets that I had missed from reading the paper.

[mycotina]

Superb, the math doesn't looks all that scary after your explanation! Now I just need pen an paper to sink it in.

[alicapwn]

excellent, clear explanation of diffusion

[vikramsandu]

I don't have enough words to describe this masterpiece. VERY WELL EXPLAINED. Thanks. :)

[sushilkhadka]

This is a great video, i completely understood till "Simplifying the Likelihood for Diffusion Models". I'll need to replay multiple times but the video is very helpful..
Please make more such video diving into maths. Most youtubers leave out the maths part while teaching DL part which is crazy because it's all math.

[learningcurveai]

Best explanation of diffusion process with connection to VAE process!

[hendrikchiche]

Great video thank you ! Some maths would need more explanation though such as at 12:59 where you assume espilon(t), epsilon(t-1), ..., espilon(0) are all the same and factorize by a new term named espilon.

[efstathiasoufleri]

Great Video! It was very helpful to understand DDPM ! Thank you so much ! : )

[arpitanand]

This video was absolutely amazing!

Also giving yourself a rating of 0.05 after spending 500 hrs on a topic is crazy(Not that I would know, because I am about a 0.0005 according to this scale)

Waiting eagerly for the next one!

[Noname-eb]

Damn, really earned that sub! Great work :)

[bhushanatote]

Hi, Very good attempt of explaining the DDPM, and thank you for sharing the information. Kudos! to answer your question at 14:22 (why reverse process is the diffusion?) because while reverse process, after the prediction of noise by u-net we check for the condition whether it is at t=0(x0-original image state) our output would be mean(has same shape of image) or not, if we are not at t=0 then our output would be mean+variance (with this variance we are adding noise again - based on x0). Hope this helps!