Understand the principles and implementation of diffusion models for generative tasks. This course covers the forward and reverse diffusion processes, network architectures like U-Net, training procedures, and methods for sampling and conditioning outputs. Build a foundational knowledge for creating AI-generated images and other data.
Prerequisites Python & ML Concepts
Level:
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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