---
license: mit
tags:
  - image-restoration
  - denoising
  - gguf
  - crispembed
  - nafnet
base_model: megvii-research/NAFNet
pipeline_tag: image-to-image
---

# NAFNet-SIDD-width32 GGUF

GGUF conversion of [megvii-research/NAFNet](https://github.com/megvii-research/NAFNet) (MIT license) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) scan cleanup.

## Model

NAFNet (Non-linear Activation Free Network) is a U-Net image restoration model that achieves state-of-the-art denoising without traditional nonlinear activations. Instead it uses SimpleGate (channel split + element-wise multiply) and Simplified Channel Attention.

- **Architecture**: U-Net with NAFBlocks
- **Config**: width=32, enc=[2,2,4,8], middle=12, dec=[2,2,2,2]
- **Channels**: 32 → 64 → 128 → 256 → 512 (middle) → 256 → 128 → 64 → 32
- **Parameters**: 29.2M
- **Training**: SIDD (Smartphone Image Denoising Dataset)
- **Performance**: PSNR 39.97 dB, SSIM 0.9599 on SIDD validation

## Files

| File | Type | Size | Notes |
|------|------|------|-------|
| `nafnet-sidd-w32-f16.gguf` | F16 | 56 MB | Full precision weights |
| `nafnet-sidd-w32-q8_0.gguf` | Q8_0 | 30 MB | Recommended |
| `nafnet-sidd-w32-q4_k.gguf` | Q4_K | 16 MB | Maximum compression |

## Usage with CrispEmbed

```bash
# CLI — preprocess scan before OCR
./build/crispembed --cleanup -m ocr_model.gguf --ocr scan.png

# Standalone cleanup
./build/crispembed --cleanup-only scan.png
```

```python
from crispembed import CrispScanCleanup

cleanup = CrispScanCleanup("nafnet-sidd-w32-q8_0.gguf")
cleaned = cleanup.process("scan.png")  # numpy RGB array
```

## Architecture Details

Each NAFBlock:
1. LayerNorm2d → Conv1x1 (c→2c) → DepthwiseConv3x3 → SimpleGate (2c→c)
2. Simplified Channel Attention (global avg pool → Conv1x1)
3. Conv1x1 (c→c) → residual connection (×beta)
4. LayerNorm2d → Conv1x1 (c→2c) → SimpleGate → Conv1x1 → residual (×gamma)

Downsampling: Conv2d stride 2, kernel 2×2.
Upsampling: Conv1x1 + PixelShuffle(2).

## License

MIT (megvii-research/NAFNet). The GGUF conversion does not change the license.

## Citation

```bibtex
@article{chen2022simple,
  title={Simple Baselines for Image Restoration},
  author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian},
  journal={arXiv preprint arXiv:2204.04676},
  year={2022}
}
```

## Provenance and EU AI Act Art. 53 note

- **Upstream model:** [megvii-research/NAFNet](https://github.com/megvii-research/NAFNet).
- **Upstream licence:** `mit`. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
- **What was done here:** format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- **Training data:** documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
- **Provider status:** under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.