Introduction to Diffusion Models for Generative AI

• Diffusion Process Mechanics

  Explain the mathematical formulation of the forward (noising) and reverse (denoising) processes.

• Model Architecture

  Understand the role and structure of the U-Net architecture in diffusion models.

• Training and Loss

  Describe the training objective and loss functions used for diffusion models.

• Sampling Techniques

  Implement sampling procedures like DDPM and understand faster alternatives like DDIM.

• Conditional Generation

  Apply basic techniques to guide the generation process using conditioning information.

• Implementation Basics

  Build and train a simple diffusion model using a deep learning framework.

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