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 [`CogVideoXPipeline`] for inference with bitsandbytes.  
```py
import torch
from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, CogVideoXTransformer3DModel, CogVideoXPipeline
from diffusers.utils import export_to_video
from transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel

quant_config = BitsAndBytesConfig(load_in_8bit=True)
text_encoder_8bit = T5EncoderModel.from_pretrained(
"THUDM/CogVideoX-2b",
subfolder="text_encoder",
quantization_config=quant_config,
torch_dtype=torch.float16,
)

quant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)
transformer_8bit = CogVideoXTransformer3DModel.from_pretrained(
"THUDM/CogVideoX-2b",
subfolder="transformer",
quantization_config=quant_config,
torch_dtype=torch.float16,
)

pipeline = CogVideoXPipeline.from_pretrained(
"THUDM/CogVideoX-2b",
text_encoder=text_encoder_8bit,
transformer=transformer_8bit,
torch_dtype=torch.float16,
device_map="balanced",
)

prompt = "A detailed wooden toy ship with intricately carved masts and sails is seen gliding smoothly over a plush, blue carpet that mimics the waves of the sea. The ship's hull is painted a rich brown, with tiny windows. The carpet, soft and textured, provides a perfect backdrop, resembling an oceanic expanse. Surrounding the ship are various other toys and children's items, hinting at a playful environment. The scene captures the innocence and imagination of childhood, with the toy ship's journey symbolizing endless adventures in a whimsical, indoor setting."
video = pipeline(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0]
export_to_video(video, "ship.mp4", fps=8)
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/cogvideox.md