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 in U-Nets
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
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
Classifier-Free Guidance (CFG): Theory and Benefits
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- Classifier-Free Diffusion Guidance, Jonathan Ho, Tim Salimans, 2022 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2207.12598 - Presents Classifier-Free Guidance, detailing its theory, training approach, and benefits compared to classifier guidance.
- Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi, 2022 arXiv preprint DOI: 10.48550/arXiv.2205.11487 - Illustrates CFG's strong performance in large-scale text-to-image generation, employing it for accurate conditional image synthesis.
- Hierarchical Text-Conditional Image Generation with CLIP Latents, Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen, 2022 arXiv preprint (arXiv) DOI: 10.48550/arXiv.2204.06125 - Applies Classifier-Free Guidance for better text-conditional image generation, showing its function in high-quality generative models.