Generative AI with Diffusion Models (GAIDM) – Outline

Gedetailleerde cursusinhoud

From U-Net to Diffusion

  • Build a U-Net architecture.
  • Train a model to remove noise from an image.

Diffusion Models

  • Define the forward diffusion function.
  • Update the U-Net architecture to accommodate a timestep.
  • Define a reverse diffusion function.

Optimizations

  • Implement Group Normalization.
  • Implement GELU.
  • Implement Rearrange Pooling.
  • Implement Sinusoidal Position Embeddings.

Classifier-Free Diffusion Guidance

  • Add categorical embeddings to a U-Net.
  • Train a model with a Bernoulli mask.

CLIP

  • Learn how to use CLIP Encodings.
  • Use CLIP to create a text-to-image neural network.