---
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