Lesson 22: Deep Learning Foundations to Stable Diffusion

[@piotr.czapla]

I have a preliminary chapters. Let's see if KZbin let me add them so that it is easier to improve on them. Chapters 00:00 - Intro 00:30 - Cosine Schedule (22_cosine) 06:05 - Sampling 09:37 - Summary / Notation 10:42 - Pedicting the noise level of noisy Fashion MNIST images (22_noise-pred) 12:57 - Why .logit() when predicting alpha bar t 14:50 - Random baseline 16:40 - mse_loss why .flatten() 17:30 - Model & results 19:03 - Why are we trying to predict the noise level? 20:10 - Training diffiusion without t - first attempt 22:58 - Why it isn’t working? 27:02 - Debugging (summmary) 29:29 - Bug in ddpm - paper that cast some light on the issue 38:40 - Kerras (Elucidating the Design Space of Diffusion - Based Generative Models) 49:47 - Picture of target images 52:48 - Scaling problem - (scalings) 59:42 - Training and predictions of modified model 1:03:49 - Sampling 1:06:05 - Sampling: Problems of composition 1:07:40 - Sampling: Rationale for rho selection 1:09:40 - Sampling: Denosing 1:15:26 - Sampling: Heun’s method fid: 0.972 1:19:00 - Sampling: LMS sampler 1:20:00 - Kerras Summary 1:23:00 - Comparison of different approaches 1:25:00 - Next lessons

[@satirthapaulshyam7769]

17:00 falttened mse