Diffusion models explained in 4-difficulty levels

In this video, we will take a close look at diffusion models. Diffusion models are being used in many domains but they are most famous for image generation. You might have seen diffusion models at work through Dall-e 2 and Imagen.

Let's look into how diffusion models learn and manage to create high-resolution, realistic images.

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#MachineLearning #DeepLearning

[pi]

'3/4/5-levels' looks like a very powerful way of explaining concepts. I'd like to see the higher levels be longer, and really drill down into the heart of the matter. So that the final level is communicating at an expert level. +1 / subbed.

[synthoelectro]

now that's some quantum technology, man... Being one of the beta testers of Stable Diffusion helps me understand this even more.

[wryltxw]

This was so helpful. Love this format of starting easier and add layers of explanations.

[yricktube]

The way you describe it is how the get the 'original' picture back. But all the content generated is new (in that combination). I was waiting for the explanation of the step that describes how a new combination (so new content) is generated into existence from the latent space through diffusion, not only the method on how to get the starting picture back from the noise...

[paramino]

This is very good intro for quick understanding of the concept 👍

[malikfahadsarwar]

It would be good if you also explain the reverse process in detail as you explained the forward process

[cosmingurau]

Sorry, but I don't understand something very important. WHY would you add the noise and then substract the noise? Correct me if I'm wrong, but the rightmost noise image in this example is basically an encoded image of the original dog image, that can be decoded deterministically with the neural network, in multiple steps. That's nice and dandy. And I do understand that the noise image is not like a RAR archive, which, were it to be slightly modified, would just yield corruption errors, and instead the modified noise image would still generate... an image. NOW.

1. How do you get from the user text prompt to the noise image of what the user WANTED, that will THEN be denoised (decoded)?
2. How is it so that not every OTHER noise result from the text prompt (except previously deterministically encoded images like this dog image for example) will output just a bunch of garbled mess? And yes, I know that is sometimes the case, I used Stable Diffusion daily.

[zhaoyufei]

really good video! I have checked few blogs explain how diffusion mode works, still can not understand. But after see your video one time, i have a better understanding how diffusion actually works! Really thanks!

[shashankshekharsingh]

Now, that's a great explanation for Diffusion Models.

[MrAlextorex]

Diffusion models actually predict a bit of noise to remove from the input noisy image at inference time. The noise is added to images just to produce training data.

[Kaleubs]

Thanks for this video, this was very insightfull. Still have a lot to learn about this topic that will revolutionize our world so much

[yousufmamsa]

Great explanation of diffusion models. Thank you.

[sotasearcher]

Such a great video to dive in! I'm live streaming learning about Diffusion, right now!

[uquantum]

Thanks so much for a useful presentation…what a good idea to present in several levels!

[John-eqcu]

I want to understand diffusion models so I can understand how it's possible for artificial intelligence to produce an image. Your explanation helps. A bit.

[sinsernadeesoyo]

This video was awesome! Well done :) and thank you

[AIMLDLNLP-TECH]

Appreciate your explanation skill.
Q. What is diffusion model
Ans. Let's say you tell your best friend, Sarah, about this amazing new flavor. Sarah gets excited and tells her friend, Tom. Then Tom tells his cousin, Emily. Emily, in turn, tells her family, and the news keeps spreading from person to person, creating a chain reaction. This process of your ice cream flavor information spreading from one person to another is like how a drop of ink spreads in water. At first, it's just a small spot, but then it spreads out and covers more and more area as time goes on.

In the diffusion model, experts study how things, whether it's information, ideas, or products like your ice cream flavor, spread through a community of people. They try to understand how fast it spreads, how many people it reaches, and what factors influence its spread. By understanding these patterns, they can learn a lot about how people share and adopt new things!

[dandogamer]

This was a great explanation! I tried to read the blog first but the maths notation was way over my head

[inetmiguel]

Nice explanation! I feel like the video title is misleading, it is just one explanation going deeper and not complete without the deeper levels of knowledge and differs a lot from other videos that start from zero the explanation at different levels. This is more like 4 shades of Diffussion :D Thanks for sharing!

[JanMatusiewicz]

Thanks for clear explanations and link to the blog!