Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference. Diffusers supports 8-bit and 4-bit quantization with bitsandbytes.
Quantization techniques that aren't supported in Transformers can be added with the [DiffusersQuantizer] class.
Learn how to quantize models in the Quantization guide.
[[autodoc]] BitsAndBytesConfig
[[autodoc]] quantizers.base.DiffusersQuantizer