| --- |
| 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. |
|
|
