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
Mathematical Underpinnings: Score Matching and ODEs
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- Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, Pieter Abbeel, 2020 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2006.11239 - The foundational paper for DDPMs, detailing the forward noising process and the reverse denoising training objective, which implicitly learns the score function.
- Generative Modeling by Estimating Gradients of the Data Distribution, Yang Song, Stefano Ermon, 2019 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.1907.05600 - Introduces score-based generative models and the principle of score matching for learning data distributions, setting the stage for diffusion models.
- Reverse-time signal processing in non-linear diffusions, Brian D. O. Anderson, 1982 International Journal of Control, Vol. 35 (Taylor & Francis) DOI: 10.1080/00207178208932997 - A foundational work establishing the concept of reverse-time stochastic differential equations, as explicitly referenced in the section content.
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