Lesson 9: Deep Learning Foundations to Stable Diffusion

We talk about some of the nifty tweaks available when using Stable Diffusion in Diffusers, and show how to use them: guidance scale (for varying the amount the prompt is used), negative prompts (for removing concepts from an image), image initialisation (for starting with an existing image), textual inversion (for adding your own concepts to generated images), Dreambooth (an alternative approach to textual inversion).

The second half of the lesson covers the key concepts involved in Stable Diffusion:
- CLIP embeddings
- The VAE (variational autoencoder)
- Predicting noise with the unet
- Removing noise with schedulers.

0:00 - Introduction
6:38 - This course vs DALL-E 2
10:38 - How to take full advantage of this course
12:14 - Cloud computing options
14:58 - Getting started (Github, notebooks to play with, resources)
20:48 - Diffusion notebook from Hugging Face
26:59 - How stable diffusion works
30:06 - Diffusion notebook (guidance scale, negative prompts, init image, textual inversion, Dreambooth)
45:00 - Stable diffusion explained
53:04 - Math notation correction
1:14:37 - Creating a neural network to predict noise in an image
1:27:46 - Working with images and compressing the data with autoencoders
1:40:12 - Explaining latents that will be input into the unet
1:43:54 - Adding text as one hot encoded input to the noise and drawing (aka guidance)
1:47:06 - How to represent numbers vs text embeddings in our model with CLIP encoders
1:53:13 - CLIP encoder loss function
2:00:55 - Caveat regarding "time steps"
2:07:04 Why don’t we do this all in one step?

[TTTrouble]

Wow this is such a treasure to have freely available and I am so thankful that you put this out for the community. Many many thanks good sir, your work towards educating the masses about AI and Machine Learning is so very much appreciated. 🎉❤

[rajahx]

This is beautifully explained Jeremy! From real basics to some of the most complicated state of the art models we have today. Bravo.

[numannebuni]

I absolutely love the style in which this is explained. Thank you very much!

[mamotivated]

Liberating the world with this quality of education

[gilbertobatres-estrada]

I am so glad you took your time to correct the math mistake! Great work! And thank you for your mission of teaching us new findings in AI and deep learning 🙏

[asheeshmathur]

Outstanding, the best description so far. God Bless Jeremy. Excellent service to curious souls.

[michaelnurse]

I thought I was going to have to wait until next year, thank you for making this content accessible

[MuhammadJaalouk]

Thank you so much for this insightful video. The lecture breaks down complex ideas into segments that are very easy to comprehend.

[kartikpodugu]

🙏🙏🙏
Amazing information.
I knew bits and pieces, now I know the entire picture.

[muppallahindu]

Thank you, Jeremy Howard for teaching me concepts of diffusion.

[AIBites]

This is a nicely thought-through course. Amazing Jeremy! :)

[ghpkishore]

That math correction was very essential to me. Coming from a mechanical background, I knew something was off, but then thought I didn't know enough about DL to figure out what it is, and that I was on the wrong. With the math correction, it clicked, and was something I knew all along.Thanks.

[chyldstudios]

Wonderful, I was waiting for these series of videos. Bravo!

[sushilkhadka]

Excellent intiution. You're doing the huge service to humanity

[sotasearcher]

28:36 - I'm here in February '24, where they are good enough to do it in 1 go with SDXL-Turbo / ADD (Nov '23) :)

[marko.p.radojcic]

I am getting YouTube premium just! to download this series. Thank you!

[akheel_khan]

Undoubtedly an accessible and insightful guide

[ricardocalleja]

Awesome material! Thank you very much for sharing

[yufengchen]

Great! I can only see 2019 version of Part 2, look foward to see the new Part 2 course available!

[cybermollusk]

You might want to put this series into a playlist. I see you have playlists for all your other courses.