Diffusion Models Course
This course equips you with essential knowledge to master diffusion models, from core theory including U-Net architectures and noise schedules to practical implementation with metrics like FID, optimisation techniques, and reliable training pipelines for developing high-performance generative AI suited for real-world applications in tech environments.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain practical skills in diffusion models, covering denoising techniques, score matching, ELBO objectives, forward and reverse processes, U-Net designs, noise schedules, samplers, dataset preparation, evaluation metrics, and optimisation strategies to confidently build, train, and assess cutting-edge generative models.
Elevify advantages
Develop skills
- Design and optimise diffusion U-Nets with conditioning and multi-scale features.
- Set up robust experiments using seeds, data loaders, logging, and repeatable workflows.
- Efficiently train and sample diffusion models by adjusting noise schedules, samplers, and loss functions.
- Optimise computational resources with mixed precision, memory management, and distributed training.
- Assess generative models using FID, IS, KID metrics, ablations, and unbiased 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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