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Advanced Diffusion Model Architectures and Training

Chapter 1: Foundation Review and Advanced Noise Schedules

Recap: Denoising Diffusion Probabilistic Models (DDPM)

Recap: Denoising Diffusion Implicit Models (DDIM)

Mathematical Underpinnings: Score Matching and ODEs

Limitations of Standard Noise Schedules

Designing Custom Noise Schedules

Learned Variance Schedules

Hands-on Practical: Implementing Noise Schedule Variants

Chapter 2: Advanced U-Net Architectures

The Standard U-Net in Diffusion Models

Attention Mechanisms in U-Nets (Self-Attention, Cross-Attention)

Integrating Time Embeddings

Advanced Conditioning Input Integration

Architectural Variants for Efficiency (Depth, Width, Pooling)

Normalization Techniques (GroupNorm, AdaLN)

Hands-on Practical: Modifying a U-Net with Attention

Chapter 3: Transformer-Based Diffusion Models

Motivation for Transformers in Generative Modeling

Adapting Transformers for Image Data (ViT, Patch Embeddings)

Diffusion Transformers (DiT): Architecture Overview

Conditioning in Diffusion Transformers

Comparison: U-Nets vs. Transformers for Diffusion

Implementation Considerations for DiTs

Hands-on Practical: Building a Simple DiT Block

Chapter 4: Advanced Training Techniques

Classifier Guidance: Principles and Implementation

Classifier-Free Guidance (CFG): Theory and Benefits

Implementing and Tuning CFG Scale

Advanced Loss Function Formulations (v-prediction, L_simple)

Model Parameterization (epsilon-prediction vs. x0-prediction)

Techniques for Training Stability (Gradient Clipping, EMA)

Mixed-Precision Training for Diffusion Models

Hands-on Practical: Implementing Classifier-Free Guidance

Chapter 5: Consistency Models

Motivation: The Need for Faster Sampling

Core Idea: Consistency Property

Consistency Model Training: Distillation Approach

Consistency Model Training: Standalone Approach

Sampling from Consistency Models (Single-step and Multi-step)

Architecture Considerations for Consistency Models

Trade-offs: Speed vs. Quality

Hands-on Practical: Basic Consistency Distillation

Chapter 6: Advanced Sampling and Optimization

Beyond DDIM: Higher-Order Solvers (DPM-Solver, UniPC)

Stochastic Sampling Variants

Guided Sampling Refinements

Troubleshooting Sampling Issues (Artifacts, Blurriness)

Model Distillation for Diffusion

Quantization of Diffusion Models

Hardware Acceleration Considerations (GPU Kernels, Compilation)

Hands-on Practical: Comparing Advanced Samplers

Advanced Diffusion Model Architectures and Training

Prerequisites: Strong foundation in diffusion model principles (DDPM/DDIM), deep learning frameworks (PyTorch/TensorFlow), and generative modeling concepts.

Level: Advanced

What You'll Learn

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.

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