While foundational sampling methods like DDPM and DDIM form the basis of generating data with diffusion models, their iterative nature often leads to slow inference times. This chapter focuses on techniques to significantly accelerate the sampling process and optimize models for practical use.

You will examine advanced sampling algorithms, including higher-order ODE solvers like DPM-Solver and UniPC, which aim to produce high-quality results in fewer steps. We will also cover stochastic sampling variants and refinements to guided sampling. Furthermore, this chapter provides practical guidance on troubleshooting common sampling issues, such as artifacts or blurriness. Finally, we will address model optimization for deployment, covering methods like model distillation, quantization, and considerations for hardware acceleration to improve speed and reduce resource consumption.