Instructions to use mlx-community/NAFNet-SIDD-width64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/NAFNet-SIDD-width64 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir NAFNet-SIDD-width64 mlx-community/NAFNet-SIDD-width64
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 956 Bytes
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license: mit
library_name: mlx
pipeline_tag: image-to-image
tags: [mlx, image-restoration, deblurring, denoising, nafnet]
base_model: megvii-research/NAFNet
---
# NAFNet width64 (MLX) — Image denoising
Apple MLX port of **[NAFNet](https://github.com/megvii-research/NAFNet)** (Simple Baselines for
Image Restoration, ECCV 2022). Runs on Apple Silicon via [MLX](https://github.com/ml-explore/mlx).
This checkpoint: **SIDD** (Image denoising). width64.
## Usage
```python
from nafnet_mlx import NAFNetConfig
from nafnet_mlx.pipeline import load_model, restore_to_file
m = load_model("model.safetensors", NAFNetConfig.sidd_width64())
restore_to_file(m, "input.png", "output.png")
```
## Validation
Faithful NHWC port (SimpleGate, Simplified Channel Attention, channel-axis LayerNorm2d,
UNet + PixelShuffle). PT-vs-MLX full-model parity on a real image ~1e-6.
## License & attribution
MIT. Derived from megvii-research/NAFNet (MIT). See `NOTICE`.
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