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---
license: mit
---
# Nunchaku
Python package for Nunchaku - post-training quantization with 4-bit weights and activations.
## Installation
```py
pip install nunchaku-0.0.2b0-cp311-cp311-linux_x86_64.whl
```
## Usage
```py
import torch
from diffusers import FluxPipeline
from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-schnell")
pipeline = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
image = pipeline("A cat holding a sign that says hello world", num_inference_steps=4, guidance_scale=0).images[0]
image.save("example.png")
```
refer to https://github.com/mit-han-lab/nunchaku for move details, this is not an official release of the pkg |