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
Hardware Acceleration Considerations (GPU Kernels, Compilation)
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- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré, 2022 Advances in Neural Information Processing Systems 35 (NeurIPS 2022), Vol. 35 DOI: 10.48550/arXiv.2205.14135 - Describes an optimized attention algorithm and its custom GPU kernel implementation for memory efficiency and speed, a prime example of custom kernel development.
- NVIDIA TensorRT Documentation, NVIDIA Corporation, 2023 (NVIDIA Corporation) - The official guide for using TensorRT to optimize and deploy deep learning models for high-performance inference on NVIDIA GPUs.
- torch.compile: Explaining PyTorch's Newest Speedup, Horace He, Michael Lazos, Jeremy Howard, Susan Sun, Edward Yang, Geeta Chauhan, Elias Ellison, Quentin Gallouédec, Daniel Hess, Christian Sarofeen, Brandon Pyper, Natalia Gimelshein, Zachary DeVito, Mike Ruberry, Peter Bell, Roman Ring, 2022 (PyTorch) - An official PyTorch blog post introducing
torch.compileand its underlying compilation mechanisms for accelerating PyTorch models with minimal code changes. - NVIDIA CUDA C++ Programming Guide, NVIDIA Corporation, 2024 (NVIDIA Corporation) - The definitive guide to parallel programming on NVIDIA GPUs using the CUDA C++ language and runtime, essential for understanding custom kernel development.
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