How Diffusion Models Work

In this video, we'll take a deep dive into the inner workings of diffusion models, the state-of-the-art approach for generating realistic and diverse images.

00:00 Introduction
00:27 How Diffusion Models Work
01:07 Denoising Images with U-Net
02:03 Noise Prediction and Removal
03:30 Sampling in Inference and Training
04:02 Time Step Encoding
04:30 Stable Diffusion and Others
05:01 Latent Diffusion
06:25 Image to Image, Inpainting, Outpainting
06:50 Generating Images with Text Prompts
08:04 Classifier-free Guidance and Negative Prompts
09:11 Conclusion

[ucnnkfl]

you have explained so well, i have seen so many videos, but, the way you explain from start to end is vary relative to what we are learning. very very good explanation about stable diffusion work-flow.

[hardslaps]

I'm sure you get a lot of comments like this, but I've been binging MKBHD vids and saw him recommend your video about compression. I watched it and was so impressed by how well you explained it and am equally impressed with this one. Especially to someone like me who has very basic understanding of the concepts. Can't wait to binge more of your videos now! Subscribed 😃

[LilianBoulard]

Cristal clear explanation, thanks a lot!

With the recent release of Meta's SAM, I was wondering it was feasible to make an improved text embedding model (i.e., CLIP) by, instead of classifying the image with a sentence, creating bounding boxes and applying a mask with different weights to indicate exactly where's a specific object in the image.
For example, in the image with the white dog on the beach, for the description "samoyed dog", pixels "making up" the dog would have a weight of 1.0, while others would have a weight of 0.

I'd be interested to know what you think, I'm quite unfamiliar with how these embedding models work :)

[Roman-kidv]

Awesome stuff man! I really wanted to know how these work but was to lazy to look it up myself. The video makes it much easier.

[ge]

added to watch later! im sure its amazing as usual

[aloglute]

Çok iyi açıklamışsın valla daha önce izlemediğim için üzüldüm ❤

[karthik]

Thanks Leo for the video, the concept of converting noised image to a clear image is understood.

How does it creates a image which doesn't exist in its training ?
It is understood that the model doesn't understand the concepts of the image and only focuses on the patterns.
But how is the below operations performed,

1. Creating a cartoon image of cat based on caption ex: Place a hat on top of cat
How does it creates a cartoon image of cat ?
How does it know the exact location of cat's head ?
How does it know to place the hat exactly at the head ?

2. A closeup shot of a dog facing the sun
How does it knows to create a close shot of a dog ?
How does it know to place the sun in the background ?
How it makes the the object to turn towards the sun ?


No videos exist to explain this concept. It would be of great help if you could make a video on this.

[baluasonnhavlog]

Xin chào bạn nhé, cảm ơn bạn đã chia sẻ, chúc sức khỏe bạn nhé, lúa chúc cả nhà xem video vui vẻ nha, chúc sức khỏe cả nhà ▶️👍👉🔔👈🤝🥰🥰🥰🥰🥰🥰🥰🥰🥰

[canxkoz]

Great explanation! Would you consider covering “DreamFusion: Text-to-3D using 2D Diffusion“ on your next video?

[north]

Merhabalar hocam bir sorum olacak sizlere
Türkiyede bir devlet ünide yazılım mı okusam bitirdikten hemen sonra yurtdışına çıksam (pasaportum var) mantıklı mı yani gelecegi var mı yoksa diş hekimliği mi mantıkı

[samabd]

Your videos are amazing and well prepared. Please could you guide me how can I becoming expert in computer vision?

[wryltxw]

I think more context would be helpful. Some parts could help with more explanation

[SnoopyDoofie]

This video doesn't show up in your YouTube channel. I got here from a web page that embeds your video. I assume you have the video as "unlisted". If you want more views, you should change it to "public", otherwise very few people will find it.