---
library_name: mlx
license: mit
license_link: https://github.com/swz30/Restormer/blob/main/LICENSE.md
base_model: swz30/Restormer
pipeline_tag: image-to-image
tags:
  - mlx
  - image-restoration
  - deblurring
  - denoising
  - restormer
---

# mlx-community/Restormer-defocus-deblurring-fp32

[Restormer](https://github.com/swz30/Restormer) — **Single-image defocus deblur** — converted to **Apple MLX** for
Apple-Silicon inference via the
[`mlx-restormer-swift`](https://github.com/xocialize/mlx-restormer-swift) Swift package.

Zamir et al., *Restormer: Efficient Transformer for High-Resolution Image Restoration*, **CVPR
2022**. **26,126,644 parameters** (494 tensors, `LayerNorm_type=WithBias`).

Out-of-focus deblur from a SINGLE image. DPDD 25.98. Notable because the high end of this task is licence-poisoned — IFAN, KPAC, DRBNet and LaKDNet are all AGPL-3.0 — so this is 0.44 dB off the best and the only permissively-licensed option.

> **Licence note.** Restormer is **plain MIT** with full commercial rights. The *same author's*
> MIRNet / MIRNetv2 / MPRNet / CycleISP carry an **Academic Public License** — easy to conflate,
> so it is worth stating explicitly.

## Use with mlx-restormer-swift

```swift
import RestormerMLXCore

var cfg = Restormer.Configuration()
cfg.normKind = .withBias
let model = Restormer(cfg)
try model.loadWeights(from: weightsURL)
let restored = model.restoreTiled(imageNHWC)   // NHWC RGB in [0,1]
```

Or as an MLXEngine `imageRestore` ModelPackage (`MLXRestormer.RestormerRestorePackage`), which
resolves this repo via the Hub.

## ⚠️ Tile — do not run this full-frame

Measured on an M5 Max: a single 1080p frame full-frame costs **15.50 GB of MLX allocation / 48.02 GB
process footprint**. Tiled, the MLX peak is **flat at ~2.6 GB** from 512² to 1080p, because the peak
is one-tile-sized — a bigger image runs *more* tiles, not bigger ones.

**Tile geometry should be 8-aligned.** Three `pixelUnshuffle(2)` stages mean the 2×2 grouping grid is
measured from the tile origin at strides 2, 4 and 8 full-resolution pixels, so an unaligned origin
shifts that grid out of phase with the full-frame decomposition.

On overlap, measured by **seam visibility** rather than PSNR: overlap 0 leaves a faint but real seam
(boundary gradient 1.31× the interior), while every aligned overlap ≥ 8 measures clean (1.09–1.20×).
Note PSNR-against-full-frame actually *prefers* overlap 0 — which is why it is the wrong metric for
a tiler.

## Conversion

MLX **NHWC** layout: conv `(O,I,kH,kW) → (O,kH,kW,I)` (186 standard + 88 depthwise), everything else
passthrough. The upstream `.pth` nests its state dict under `raw['params']` (BasicSR convention).

## Parity

Gated against the PyTorch oracle on the CPU stream, fp32, judged on relative error:

- **key contract** — 494 tensors / 26,126,644 params / 0 missing / 0 unused, strict load
- **primitives** — both LayerNorm variants, and the pixel-shuffle pair (worst 2.6e-06)
- **blocks** — MDTA, GDFN, TransformerBlock (worst 2.6e-07)
- **full model** — cosine **1.00000000** at 64² / 128² / 256² (worst 1.0e-06)

The pixel-shuffle gate also runs the *wrong* channel ordering — the `(r,r,C)` split that compiles
and silently scrambles the image — and measures it at **493,451× the observed rounding**, so the
tolerance keeps ~5 orders of discrimination.

## Honest limitation

Restormer's attention is spatially **global**, so it is weak on *spatially varying* blur. Benchmark
rank is also a weak predictor of real-world value: *"Deblurring in the Wild"* (2026) found every
GoPro-trained method scored below the blurry input on real smartphone blur. Validate on your own
photographs.

Weights: MIT (swz30/Restormer, first-party GitHub release v1.0). Port code: MIT.