A Guide to Quantization Backends in Diffusers

Diffusers now support multiple quantization backends to improve model efficiency. This article explores the available options, their trade-offs, and how to choose the right one for your needs. Quantization reduces model size and speeds up inference by lowering the precision of weights. Popular backends include ONNX Runtime, TensorRT, and Intel Neural Compressor. Each offers unique optimizations: ONNX Runtime provides broad compatibility, TensorRT excels on NVIDIA GPUs, and Intel Neural Compressor targets CPU deployments. We compare performance metrics, ease of integration, and supported hardware, helping developers make informed decisions when deploying diffusion models.