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](https://huggingface.co/docs/bitsandbytes/en/index).  
Quantization techniques that aren't supported in Transformers can be added with the [`DiffusersQuantizer`] class.  
<Tip>  
Learn how to quantize models in the [Quantization](../quantization/overview) guide.  
</Tip>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/quantization.md