Diffusion Models Course
This comprehensive course on diffusion models takes you from foundational theory to practical deployment. Explore U-Net architectures, noise scheduling, key metrics such as FID and IS, plus optimisation techniques to create efficient, high-performing generative AI solutions suitable for real-world applications in technology and research.

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.What our students say
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