Zero-DCE — LiteRT (TFLite) GPU, FP16
On-device LiteRT (.tflite) conversion of
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 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)
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.
License & attribution
- License: Apache-2.0 (© the Zero-DCE authors, 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.
Want a different model on-device? Open a request — free, open weights only; the export and its measured numbers get published publicly.