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Diffusion Models Course

Diffusion Models Course
flexible workload of 4 to 360h
valid certificate in your country

What will I learn?

Gain expertise in diffusion models through this practical course covering probabilistic basics, denoising techniques, score matching, ELBO objectives, forward-reverse processes, U-Net designs, noise schedules, samplers, dataset preparation, evaluation metrics, and optimisation strategies to confidently build, train, and assess advanced generative models.

Elevify advantages

Develop skills

  • Design and optimise diffusion U-Nets with conditioning and multi-scale features.
  • Set up reliable experiments using seeds, data loaders, logging, and repeatable workflows.
  • Efficiently train and sample diffusion models by adjusting noise schedules, samplers, and loss functions.
  • Maximise computational efficiency with mixed precision, memory optimisation, and distributed training.
  • Assess generative models accurately using FID, IS, KID metrics, ablations, and unbiased comparisons.

Suggested summary

Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.
Workload: between 4 and 360 hours

What our students say

Your classes are perfect. I got the one-year package and finally have the chance to follow several topics I'm interested in without having to switch platforms... I appreciate everything you do, I've already recommended you to others...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons are straight to the point and how I can switch chapters and skip content I don't need.
Mariana Ferres
Mariana FerresPhotography Student
I like the content and the way the videos are presented and transcribed, which speeds up the process!
Luciana Alvarenga
Luciana AlvarengaNail Design Student
The platform is fast and easy to use. The variety of content and the supplementary videos really help with learning.
Giulio Carlo
Giulio CarloPrompt Engineering Student

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