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README.md

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+---
+license: apache-2.0
+library_name: litert
+pipeline_tag: image-to-image
+base_model: sayakpaul/zero-dce
+tags:
+  - litert
+  - tflite
+  - on-device
+  - android
+  - low-light-enhancement
+  - image-enhancement
+  - zero-dce
+  - gpu
+---
+
+# Zero-DCE — LiteRT (TFLite) GPU, FP16
+
+On-device [LiteRT](https://ai.google.dev/edge/litert) (`.tflite`) conversion of
+**[Zero-DCE](https://github.com/Li-Chongyi/Zero-DCE)** (Zero-Reference Deep Curve
+Estimation) for **low-light image enhancement**. The DCE-Net is a tiny 7-layer CNN
+that estimates pixel-wise tone curves; the curves are applied iteratively (8×) to
+brighten the image. The whole pipeline (curve estimation **and** the iterative
+application) is baked into the graph, so the model takes a dark image and returns the
+enhanced image directly.
+
+The model runs **fully on the LiteRT `CompiledModel` GPU accelerator** (ML Drift):
+every op is GPU-native, no CPU fallback, no Flex/Custom ops. Converted with
+[`litert-torch`](https://github.com/google-ai-edge/ai-edge-torch) **with no patches**.
+
+## Files
+
+| File | Precision | Size |
+|------|-----------|------|
+| `zerodce_512_fp16.tflite` | fp16 weights | ~0.18 MB |
+| `zerodce_512.tflite` | fp32 | ~0.34 MB |
+
+## I/O
+
+- **Input**: `[1, 512, 512, 3]` float32, **NHWC**, RGB, range **`[0, 1]`** (just divide
+  by 255 — no mean/std normalization).
+- **Output**: `[1, 512, 512, 3]` float32, **NHWC**, RGB, range `[0, 1]` — the enhanced
+  image. Multiply by 255 to display.
+
+## Ops
+
+The graph lowers entirely to GPU-clean builtins — the iterative curve application
+`x = x + r·(x² − x)` is written as `x*x` so it becomes `MUL`, not `POW`:
+
+```
+CONV_2D x7, MUL x16, SUB x8, ADD x8, SLICE x8, CONCATENATION x3, TANH x1
+```
+
+No `GATHER_ND`, no Flex/Custom, no >4D reshapes.
+
+## Fidelity
+
+- Converted fp32 vs original PyTorch: **corr 1.0000**, max|diff| ~2e-5.
+- fp16 vs fp32: **corr 1.0000**, max|diff| ~3e-5.
+
+## On-device (Pixel 8a, verified)
+
+The fp16 model compiles to **58 / 58 nodes on the LiteRT GPU delegate (LITERT_CL)** —
+full GPU residency, no CPU fallback.
+
+## Usage (Android, LiteRT CompiledModel)
+
+```kotlin
+val model = CompiledModel.create(
+    context.assets, "zerodce_512_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null
+)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(rgbFloats01)   // [1,512,512,3] interleaved RGB in [0,1]
+model.run(inputs, outputs)
+val enhanced = outputs[0].readFloat()  // [1,512,512,3] in [0,1]
+```
+
+A complete Android sample (live camera + gallery low-light enhancement) is available in
+[google-ai-edge/litert-samples](https://github.com/google-ai-edge/litert-samples).
+
+## License & attribution
+
+- License: **Apache-2.0** (© the Zero-DCE authors,
+  [Li-Chongyi/Zero-DCE](https://github.com/Li-Chongyi/Zero-DCE)). Original work:
+  Guo et al., *"Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement"*,
+  CVPR 2020. This is a format conversion of the official weights (no architectural
+  changes); all credit to the original authors.