Chapter 4: Advanced Training Techniques

While sophisticated architectures are essential for diffusion models, effective training procedures are equally critical for achieving high-quality generation. This chapter moves beyond basic training loops to cover techniques that enhance sample quality, improve stability, and provide finer control over the generation process.

We will cover:

Classifier-Free Guidance (CFG): Implementing and tuning CFG to guide generation using the model itself, adjusting the guidance strength $s$ .
Advanced Loss Functions and Parameterizations: Understanding objectives like $v$ -prediction and comparing model outputs parameterized as noise ( $\epsilon$ ) versus original data ( $x_0$ ).
Training Stability: Applying techniques such as gradient clipping and Exponential Moving Average (EMA) of weights to ensure smoother convergence.
Training Efficiency: Utilizing mixed-precision (FP16/BF16) arithmetic to accelerate training and reduce memory usage.

Upon completing this chapter, you will understand how to apply these advanced training methods to build and refine high-performance diffusion models.

Sections

4.1 Classifier Guidance: Principles and Implementation
4.2 Classifier-Free Guidance (CFG): Theory and Benefits
4.3 Implementing and Tuning CFG Scale
4.4 Advanced Loss Function Formulations (v-prediction, L_simple)
4.5 Model Parameterization (epsilon-prediction vs. x0-prediction)
4.6 Techniques for Training Stability (Gradient Clipping, EMA)
4.7 Mixed-Precision Training for Diffusion Models
4.8 Hands-on Practical: Implementing Classifier-Free Guidance