Quantization helps reduce the memory requirements of very large models by storing model weights in a lower precision data type. However, quantization may have varying impact on video quality depending on the video model.  
Refer to the [Quantization](../../quantization/overview) overview to learn more about supported quantization backends and selecting a quantization backend that supports your use case. The example below demonstrates how to load a quantized [`AuraFlowPipeline`] for inference with bitsandbytes.  
```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, AuraFlowTransformer2DModel, AuraFlowPipeline
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel

quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"fal/AuraFlow",
subfolder="text_encoder",
quantization_config=quant_config,
torch_dtype=torch.float16,
)

quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = AuraFlowTransformer2DModel.from_pretrained(
"fal/AuraFlow",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.float16,
)

pipeline = AuraFlowPipeline.from_pretrained(
"fal/AuraFlow",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)

prompt = "a tiny astronaut hatching from an egg on the moon"
image = pipeline(prompt).images[0]
image.save("auraflow.png")
```  
Loading [GGUF checkpoints](https://huggingface.co/docs/diffusers/quantization/gguf) are also supported:  
```py
import torch
from diffusers import (
AuraFlowPipeline,
GGUFQuantizationConfig,
AuraFlowTransformer2DModel,
)

transformer = AuraFlowTransformer2DModel.from_single_file(
"https://huggingface.co/city96/AuraFlow-v0.3-gguf/blob/main/aura_flow_0.3-Q2_K.gguf",
quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
torch_dtype=torch.bfloat16,
)

pipeline = AuraFlowPipeline.from_pretrained(
"fal/AuraFlow-v0.3",
transformer=transformer,
torch_dtype=torch.bfloat16,
)

prompt = "a cute pony in a field of flowers"
image = pipeline(prompt).images[0]
image.save("auraflow.png")
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/aura_flow.md