While Chapter 1 outlined the architectural challenges of deploying diffusion models, this chapter focuses on the core model itself. The iterative nature of the diffusion process, often involving hundreds of steps passing data through a large neural network, creates significant computational demands during inference. This results in challenges related to latency, throughput, and cost.

This chapter introduces techniques to mitigate these performance bottlenecks:

• Analyze Bottlenecks: Pinpoint the most resource-intensive parts of the diffusion sampling process.
• Model Compression: Apply methods like quantization (reducing precision to formats like $INT8$ or $FP16$) and knowledge distillation to create smaller, faster models.
• Sampler Efficiency: Explore strategies to decrease the number of sampling steps required for generation without substantial quality loss.
• Hardware and Compiler Optimization: Learn to effectively use GPUs/TPUs and leverage compilers such as TensorRT and OpenVINO for optimized execution graphs.
• Performance Measurement: Establish rigorous benchmarking procedures to quantify improvements in latency, throughput, and cost.

Through detailed explanations and a hands-on practical section, you will gain the skills to significantly improve the inference efficiency of diffusion models.