Large diffusion models like Flux can generate impressive images but require substantial memory, making quantization an attractive option to reduce their size without significantly affecting output quality. The article discusses various quantization backends available in Hugging Face Diffusers, including bitsandbytes, torchao, and Quanto, and provides examples of how to implement these quantizations to optimize memory usage and performance in image generation tasks.