Diffusion Model Quantization: A Review

Understand

Recent success of large text-to-image models has empirically underscored the exceptional performance of diffusion models in generative tasks.

• To facilitate their efficient deployment on resource-constrained edge devices, model quantization has emerged as a pivotal technique for both compression and acceleration.
• This survey offers a thorough review of the latest advancements in diffusion model quantization, encapsulating and analyzing the current state of the art in this rapidly advancing domain.
• First, we provide an overview of the key challenges encountered in the quantization of diffusion models, including those based on U-Net architectures and Diffusion Transformers (DiT).