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
Quantization of Diffusion Models
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- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference, Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko, 2018 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR.2018.00216 - This paper introduced Quantization-Aware Training (QAT), a technique discussed in the section, providing a foundational understanding of how models can be trained to be robust to quantization.
- Q-Diffusion: Quantizing Diffusion Models for Efficient Generation, Yefei He, Hanyu Wang, Xiangyu Sun, Jianxing Xu, Qingyi Gu, Yang Liu, Zhaodong Wang, Zhangyang Wang, Kaiyuan Guo, and Wenshuo Li, 2023 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Institute of Electrical and Electronics Engineers (IEEE)) DOI: 10.1109/CVPR52688.2023.00762 - This paper directly addresses the challenges and proposes solutions for quantizing diffusion models, offering insights into practical implementation and strategies for maintaining generative quality.
- Outlier-Aware Quantization for Diffusion Models, Qingyi Gu, Yefei He, Fan Yang, Yihua Ye, Jianxing Xu, Zhangyang Wang, Kaiyuan Guo, Wenshuo Li, 2023 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE) DOI: 10.1109/CVPR52688.2023.00761 - This work specifically tackles the issue of dynamic ranges and outliers in diffusion model activations, a major challenge highlighted in the section, providing advanced techniques for more effective quantization.