Lesson 25: Deep Learning Foundations to Stable Diffusion

Then Jeremy wraps up "Stable diffusion from scratch" by showing how to use the latents in a variational encoder as the "pixels" in a regular diffusion model. He also describes an intriguing new idea for students to follow up: what if you use latents for other purposes, such as a classification model? Perhaps this would open up a whole world of possibilities, such as latents-FID, latents-perceptual-loss, and new approaches to diffusion guidance!

[kashifsiddhu]

00:02 Introduction to NLP and delay in completing stable diffusion
02:19 Creating a subset dataset from longer call recordings for deep learning analysis
06:16 Mel spectrogram focuses on human audible frequencies and transforms them into a log space.
08:26 Converting audio to spectrogram and back to audio
12:46 The model uses Transformer blocks for stable diffusion
14:58 Generating fake bird calls with spectrogram diffusion
19:23 Creating a simple autoencoder with a single hidden layer MLP
21:35 The simple autoencoder compresses and regenerates images.
26:00 Log variance affects standard deviation in deep learning
28:10 Utilizing BCE loss for deeper learning stability
32:21 Minimize log variance in deep learning foundations
34:47 Mapping inputs to a restricted range for better decoding
38:39 Introducing new metrics for model evaluation
40:45 VAE benefits from pre-trained models for efficient generation
44:51 Creating a data set and pre-processing images for deep learning
47:02 Using parallel processing to speed up image reading in deep learning
51:14 Discussion on spatial resolution and training objectives
53:21 Deep learning foundations include perceptual loss and adversarial loss
57:54 Pre-training generator and discriminator for GANs
59:49 Using memory mapped numpy files to save latents efficiently
1:04:01 Creating memory-mapped numpy array of latents
1:06:02 Training and validation set creation
1:10:06 Creating high-quality 256 by 256 pixel images in a few hours with stable diffusion VAE
1:11:56 Experimenting with diffusers and stable diffusion models for better results.
1:16:03 Data set acquisition process explained
1:18:01 Creating a cache for quicker access to files
1:22:11 Preparing and transforming training data for deep learning
1:23:58 Implementing data augmentation techniques in deep learning training process
1:27:47 Achieved 66% accuracy after 40 epochs of training a new model
1:30:07 Pre-training with perceptual loss yields promising results
1:33:40 Congratulations on completing the course, consider experimenting and collaborating further
1:35:36 Deep Learning Foundations to Stable Diffusion
Crafted by Merlin AI.

[ADHDOCD]

Amazing lecture as always! Cannot wait for the LLM lectures. MIT's lectures pale in comparison to what Jeremy and his team produce.

[hacklife]

How do I join the live classes for LLM?

[shubh]

Please roll out the next series, although I'm in the first part, I just can't wait to reach here and learn from such amazing tutors.

[starlite]

Thank you so much for this course. Even though I don't have the time to try things on my own, I noted down 207 useful things for me.

[ankile]

When is the next episodes coming out, does anyone know?

[pj-nznm]

this course is literly overwhelming for me, sometimes I felt sometime its not for me even this lecture also. it's hard