Diffusion Models Course
This course provides a practical guide to diffusion models, covering foundations, implementation, training, and evaluation techniques for building high-quality generative models.

flexible workload from 4 to 360h
valid certificate in your country
What will I learn?
This short, practical Diffusion Models Course gives you a clear path from probabilistic foundations to high-quality implementations. You will learn denoising, score matching, ELBO objectives, forward and reverse processes, U-Net architectures, noise schedules, and samplers, plus dataset setup, evaluation metrics, and optimization tricks so you can design, train, and benchmark modern diffusion models with confidence.
Elevify advantages
Develop skills
- Build diffusion U-Nets: design, condition, and optimise multi-scale generators.
- Engineer robust experiments: seeds, loaders, logging, and reproducible pipelines.
- Train and sample diffusion models: tune noise schedules, samplers, and losses fast.
- Optimise compute: mixed precision, memory tricks, and distributed diffusion training.
- Evaluate generative models: FID, IS, KID, ablations, and fair model comparisons.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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