Training A Diffusion Model - Stable Diffusion Masterclass

This is a preview lesson from the deeplizard Stable Diffusion Masterclass!
Welcome to this deeplizard course, Stable Diffusion Masterclass - Thoery, Code, & Application! In this course, we learn the theory behind Stable Diffusion and get hands on experience with code and applications.
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[@marufahmed3416]

Really good explanation, please continue to produce these videos.

[@deeplizard]

✨ See the course page to get an overview of the course and enroll:
🔗 deeplizard.com/course/dicpailzrd
✨ Check out more course details and other resources in the course intro:
🔗 deeplizard.com/lesson/dia1aidrzl

[@leanwolf6667]

Thank you!

[@stefanobraghettocatoni1464]

I have a question. Is fair to use the same noise scheduler for inference than for training? I am asking because when the model is predicting noise during training it has just the correspond noise step acording to that timestep. But when inference, the model has 2 noises: The timestep noise (same as training) + noise that wasn't able to delete since the model isn't perfect and always will make tiny mistakes. For diffusion process with a lot of steps I fell is unfair to treat both as the same.
Thank you for your clarification!

[@alikadhm1903]

we need more discount for this course, it’s expensive compared with udemy

[@shanhuang5770]

This is really explained poorly in that
1. It assigns unnecessary complexity: rather than say the model subtracts the predicted noise and adds it back, you could have just said it subtracts 10% of the predicted noise.
2. The explanation goes on and on with the computation steps without explaining the intuition.
3. It is simply incomplete. You did not explain what are we doing with noise scheduler with multiple “t”s, if during training, the model is already learning denotes in incremental steps

[@Ezghoul1]

Of course... they are trying to sell a course 😅