| --- |
| 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. |
| |
