Part 1 of 2: Generative AI with Diffusion Models

This two-part workshop will dive deeper into denoising diffusion models, which are a popular choice for text-to-image pipelines.

Applications of this technology include creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more. Each session is roughly divided into 3 hours of active teaching and 1 hour of extra Q&A.

Three components below will spread over the two sessions:


U-Net architecture and training a model to remove noise from an image.
Autoencoders and variational autoencoders and then go toward diffusion models.
Diffusion models and how to train them.


At the end of course, you can obtain a NVIDIA Certificate if you pass the assessment.