| --- |
| library_name: mlx |
| license: mit |
| license_link: https://github.com/Fediory/HVI-CIDNet/blob/master/LICENSE |
| base_model: Fediory/HVI-CIDNet |
| pipeline_tag: image-to-image |
| tags: |
| - mlx |
| - low-light-enhancement |
| - exposure-correction |
| - image-enhancement |
| - hvi-cidnet |
| --- |
| |
| # mlx-community/HVI-CIDNet-Generalization-fp32 |
|
|
| [HVI-CIDNet](https://github.com/Fediory/HVI-CIDNet) low-light / exposure correction, converted to |
| **Apple MLX** for Apple-Silicon inference via the |
| [`mlx-cidnet-swift`](https://github.com/xocialize/mlx-cidnet-swift) Swift package. |
|
|
| Yan et al., *HVI: A New Color Space for Low-light Image Enhancement*, **CVPR 2025** — 1st place, |
| NTIRE 2025 Low-Light Enhancement Challenge. 1,975,569 parameters (7.9 MB). Underexposed image in → |
| re-exposed image at the same resolution out. |
|
|
| **Recommended default.** Trained for cross-dataset generalization. Does the least damage when the exposure gate mis-fires (23.37 dB on an already-correct exposure, vs 20.99 / 16.10 for the others). |
|
|
| ## Use with mlx-cidnet-swift |
|
|
| ```swift |
| import CIDNetMLXCore |
| |
| let model = CIDNet() |
| try model.loadWeights(from: weightsURL) // model.safetensors from this repo |
| let brightened = model(imageNHWC) // NHWC RGB in [0,1] |
| ``` |
|
|
| Or as an MLXEngine `imageRelight` ModelPackage (`MLXCIDNet.CIDNetRelightPackage`), which resolves |
| this repo via the Hub and applies the exposure gate below automatically. |
|
|
| ## ⚠️ Gate this model on input exposure — it is not safe to apply unconditionally |
|
|
| Every published checkpoint drives its output toward a **target mean luma regardless of input**. On |
| an image that is already correctly exposed the model therefore *degrades* it. Measured against a |
| correctly-exposed reference: |
|
|
| | checkpoint | applied to an already-correct exposure | |
| |---|---| |
| | Generalization | 23.37 dB | |
| | LOLv1-wperc | 20.99 dB | |
| | SICE | **16.10 dB** | |
|
|
| SICE's output mean stays within 0.35–0.40 across a **12× input range** — it is an auto-exposure |
| normalizer, not a shadow lift. |
|
|
| Estimate input luma and **bypass above a threshold**; expose a strength blend for partial |
| application. `mlx-cidnet-swift` does both. Note also that Generalization scores **−0.52 dB — worse |
| than doing nothing** — at 0.70× exposure, which is the *moderate* underexposure regime that |
| LOL-trained models are documented to handle badly. |
|
|
| ## Conversion |
|
|
| MLX **NHWC** layout: 191 tensors → 81 conv + 61 depthwise transposed `(O,I,kH,kW) → (O,kH,kW,I)`, |
| 49 passthrough. Upstream already publishes safetensors, so this is a relayout, not a format change. |
|
|
| `trans.density_k` is a **learned** parameter and differs per checkpoint (this one: **0.9835**; init is |
| 0.2). It rides in the weights — do not hardcode it. |
|
|
| ## Parity |
|
|
| Gated against the PyTorch oracle on the CPU stream, fp32, judged on relative error: |
|
|
| - **key contract** — 191 tensors / 1,975,569 params / 0 missing / 0 unused, strict load |
| - **HVI colour transform** — **bit-exact** (worst 6.6e-07), including the degenerate colours and |
| the yellow/cyan channel *ties* that decide the masking priority |
| - **primitives** — PReLU exactly 0.00e+00; the align-corners resamplers ~5e-07 |
| - **full model** — cosine **1.00000000** at 64² / 128² / 256², and on a severely underexposed tile |
|
|
| Weights: MIT (Fediory/HVI-CIDNet, published first-party by the authors). Port code: MIT. |
|
|
