DDColor tiny ONNX 512x512 int8 + model card

README.md

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 ---
 license: apache-2.0
+library_name: onnx
+pipeline_tag: image-to-image
+base_model: piddnad/ddcolor_paper_tiny
+tags:
+  - colorization
+  - image-colorization
+  - onnx
+  - onnxruntime-web
+  - int8
 ---
+
+# DDColor tiny, ONNX 512x512 (int8)
+
+An ONNX export of **DDColor paper-tiny** for automatic image colorization, built for in-browser use with
+onnxruntime-web. It powers the Colorize neural filter in the ToolsMonk Photo Editor, where it runs entirely on
+the user's device.
+
+## Provenance
+
+- Model and code: [piddnad/DDColor](https://github.com/piddnad/DDColor) (Apache-2.0), weights
+  `ddcolor_paper_tiny` ([piddnad/ddcolor_paper_tiny](https://huggingface.co/piddnad/ddcolor_paper_tiny)).
+- Architecture settings match the upstream `scripts/export_onnx.py` for `model_size=tiny`: ConvNeXt-T encoder,
+  MultiScaleColorDecoder, 100 queries, 3 scales, 9 decoder layers, input 512x512.
+- Exported with `torch.onnx.export`, opset 17, fixed input shape `[1, 3, 512, 512]`.
+- Weights of MatMul and Gemm nodes quantized to uint8 with `onnxruntime.quantization.quantize_dynamic`
+  (fp32 export 257.7 MiB, this file 107.5 MiB). Mean absolute difference against the fp32 export on a test
+  image: about 1.1 ab units.
+
+## File
+
+| File | Bytes | SHA-256 |
+|---|---|---|
+| `ddcolor_tiny_512_int8.onnx` | 112,723,138 | `e983347272b8e218edc974a0af2bd66f53b73212abb800e103c64806e4c14a14` |
+
+## Input and output
+
+- `input`: float32 `[1, 3, 512, 512]`, RGB in 0..1. Feed a grey image: take the CIELAB lightness L of the photo,
+  set a = b = 0 and convert back to RGB.
+- `output`: float32 `[1, 2, 512, 512]`, the predicted CIELAB a and b planes.
+
+To colorize, resize the predicted a/b planes to the original size and combine them with the original photo's
+L channel, then convert Lab to RGB. Detail and brightness come from the original photo.
+
+## Licence
+
+Apache License 2.0, the same licence as the upstream DDColor code and weights. See the upstream repository for
+the paper and citation:
+
+> Kang et al., "DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders", ICCV 2023.

ddcolor_tiny_512_int8.onnx

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