Gain proficiency in sophisticated diffusion model architectures and advanced training methodologies. This course covers complex U-Net variations, transformer-based diffusion, consistency models, advanced conditioning, and optimization strategies for state-of-the-art generative modeling.
Prerequisites Diffusion Model Basics & Python
Level:
Advanced Architectures
Implement and analyze complex U-Net variants and transformer-based architectures for diffusion models.
Consistency Models
Understand the theory and practical implementation of consistency models for faster sampling.
Sophisticated Training Techniques
Apply advanced training strategies, including refined noise schedules, classifier-free guidance scaling, and parameterization methods.
Advanced Conditioning Mechanisms
Implement complex conditioning methods beyond simple text or class labels, such as cross-attention modifications and compositional generation.
Sampling and Optimization
Master advanced sampling algorithms, troubleshoot convergence issues, and optimize models for speed and memory efficiency.
Model Evaluation
Evaluate the performance of advanced diffusion models using appropriate metrics and qualitative analysis.