Detailed Course Outline
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.