README.md

---
license: mit
library_name: LiteRT
pipeline_tag: image-to-image
tags:
  - litert
  - tflite
  - on-device
  - android
  - gpu
  - image-restoration
  - denoising
  - nafnet
base_model: megvii-research/NAFNet
---

# NAFNet-SIDD-width32 — LiteRT (on-device image denoising, fully-GPU)

[NAFNet](https://github.com/megvii-research/NAFNet) (Nonlinear Activation Free Network, ECCV 2022) image
restoration, converted to **LiteRT** and running **fully on the `CompiledModel` GPU** (ML Drift) on Android.
This is the **SIDD-width32** variant — real-image **denoising**. NAFNet is a U-Net of NAFBlocks with **no
activation functions** (SimpleGate = channel-split multiply), so the whole network is a clean CNN on the GPU.

![NAFNet-SIDD — noisy input | denoised (on-device LiteRT GPU)](samples/sample.png)

## On-device (Pixel 8a, Tensor G3 — verified)

| | |
|---|---|
| nodes on GPU | **2179 / 2179** LITERT_CL (full residency) |
| inference | **~46 ms** (256×256) |
| size | 62.5 MB (fp16) |
| accuracy | device output **== PyTorch (corr 0.999999)** — re-authoring is numerically exact |

```
image[1,3,256,256] (RGB [0,1]) →[GPU: NAFNet U-Net]→ denoised[1,3,256,256]
```

## How it converts (litert-torch)

Pure CNN (no activations). Three numerically-exact re-authorings, the headline being **SafeLayerNorm**:
NAFNet's residual stream grows large (|x|≈175 at the bottleneck), so the LayerNorm channel reductions
`Σ_c x` and `Σ_c (x−μ)²` (~15M) **overflow fp16 (max 65504)** on the Mali delegate (which computes in fp16
regardless of the model dtype) → a grid artifact. Doing the reductions in a down-scaled `x/S` domain (S=128)
and rescaling is exact and fp16-safe. Plus the Simplified Channel Attention `AdaptiveAvgPool2d(1)` →
`mean(3).mean(2)`, and the upsample `Conv2d(1×1)+PixelShuffle(2)` → depth-to-space `ZeroStuffConvT2d`.

Result: banned ops NONE, all tensors ≤4D, tflite-vs-torch corr **1.0**, device-vs-torch corr **1.0**.

A complete Android sample (image picker + before/after) is in the official
[google-ai-edge/litert-samples](https://github.com/google-ai-edge/litert-samples) repo under
`compiled_model_api/image_restoration` (push this `.tflite` in place of the deblur model).

## License

[MIT](https://github.com/megvii-research/NAFNet/blob/main/LICENSE). Upstream:
[megvii-research/NAFNet](https://github.com/megvii-research/NAFNet); weights NAFNet-SIDD-width32.