Add DDColor paper tiny ONNX (colourisation), update model card and licences

LICENSE

@@ -621,3 +621,209 @@ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+================================================================
+DDColor (piddnad) - Apache-2.0
+Source: https://github.com/piddnad/DDColor/blob/2adb63f2656ac41cbdf7b894cddd94121a3faf13/LICENSE
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README.md

@@ -12,6 +12,8 @@ tags:
 - face-restoration
 - super-resolution
 - background-removal
+- colorization
+- ddcolor
 - on-device
 ---
 
@@ -32,11 +34,13 @@ Pin a commit in your download URL (`/resolve/<commit>/<file>`) and verify the SH
 | `realesr-general-x4v3.onnx` | 4,866,420 | `7b10e80021ec3e0ff35017ae5022791879d93a799b2e201e1010a37510202266` | Real-ESRGAN general x4v3 (SRVGGNetCompact), fp32 | Unmodified copy of `onnx/model.onnx` from [vlad-grig/realesr-general-x4v3-onnx](https://huggingface.co/vlad-grig/realesr-general-x4v3-onnx) (commit `0e95d93e454cf6b5de9bdf5a1c9e7ec340d38231`), an export of the official `realesr-general-x4v3.pth` from [xinntao/Real-ESRGAN release v0.2.5.0](https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.5.0) | BSD-3-Clause, © 2021 Xintao Wang |
 | `u2netp.onnx` | 4,574,861 | `309c8469258dda742793dce0ebea8e6dd393174f89934733ecc8b14c76f4ddd8` | U²-Netp (small) salient-object matte (default background model) | Unmodified copy of `u2netp.onnx` from [danielgatis/rembg release v0.0.0](https://github.com/danielgatis/rembg/releases/tag/v0.0.0) (rembg is MIT), converted from the weights of [xuebinqin/U-2-Net](https://github.com/xuebinqin/U-2-Net) | Apache-2.0, © Xuebin Qin et al. |
 | `u2net.onnx` | 175,997,641 | `8d10d2f3bb75ae3b6d527c77944fc5e7dcd94b29809d47a739a7a728a912b491` | U²-Net (full) salient-object matte (HD option) | Unmodified copy of `u2net.onnx` from [danielgatis/rembg release v0.0.0](https://github.com/danielgatis/rembg/releases/tag/v0.0.0) (rembg is MIT), converted from the weights of [xuebinqin/U-2-Net](https://github.com/xuebinqin/U-2-Net) | Apache-2.0, © Xuebin Qin et al. |
+| `ddcolor-paper-tiny.onnx` | 220,552,834 | `5479b1b00d8ba2b3c312e5378e75dc93eac6f80c8497e90169801896e1eeb68d` | DDColor-T (`ddcolor_paper_tiny`), ConvNeXt-T encoder + multi-scale colour decoder, fp32, opset 17 (photo colourisation) | Built by MomoApps from the official weights [piddnad/ddcolor_paper_tiny](https://huggingface.co/piddnad/ddcolor_paper_tiny) (commit `cf9fd99c1d7472689ec7413441c1b799a51866a3`, `pytorch_model.bin` sha256 `8a1277bc90a1bfbb6d2d83933a9a6bc821931879ca93e26e4fcec12165d41fce`) and the architecture in [piddnad/DDColor](https://github.com/piddnad/DDColor) (commit `2adb63f2656ac41cbdf7b894cddd94121a3faf13`). The weights load with no missing or unexpected keys. The 8 spectral-norm layers are folded into plain weights, which is exact in eval mode. Exported with `torch.onnx.export` (torch 2.15.0). | Apache-2.0, © Xiaoyang Kang et al. (DAMO Academy, Alibaba) |
 
 ## Input and output contracts
 
 - **GFPGAN (both files):** input `input`, float32 `[1,3,512,512]` RGB in −1..1 (one aligned face crop). Output `output`, the same shape and range.
 - **Real-ESRGAN general x4v3:** input `input`, float32 `[1,3,H,W]` RGB in 0..1, with dynamic H and W. Output `output`, `[1,3,4H,4W]`. Clamp the output to 0..1.
+- **DDColor paper tiny:** input `input`, float32 `[1,3,512,512]` RGB in 0..1. Build it as DDColor's `ColorizationPipeline` does: resize the photo to 512×512, convert to Lab (OpenCV float), keep L, set a = b = 0, and convert back to RGB. The result is the greyscale L channel replicated as RGB. Output `output`, float32 `[1,2,512,512]`: the predicted Lab a and b channels on OpenCV's float Lab scale (roughly −128..127; clamp them). Resize a and b to the original size, combine them with the original L, and convert Lab to RGB. Ops include `Einsum`, `Erf` and `LayerNormalization`, which the full ONNX Runtime Android package supports. A reduced-operator ORT Mobile build must include them.
 - **U²-Net / U²-Netp:** input `input.1`, float32 `[1,3,320,320]` RGB, with ImageNet normalisation applied after dividing by the peak pixel. The first output is the matte.
 
 ## Training data note: FFHQ
@@ -46,11 +50,17 @@ GFPGAN was trained on the [FFHQ](https://github.com/NVlabs/ffhq-dataset) face da
 The GFPGAN code and weights are released under Apache-2.0. Whether the dataset's terms carry over to the trained weights is legally unsettled.
 Commercial users of the GFPGAN files should make their own assessment.
 
+## Training data note: ImageNet
+
+DDColor `ddcolor_paper_tiny` was trained on [ImageNet](https://www.image-net.org/) (ILSVRC 2012), according to the DDColor model zoo. ImageNet's terms of access limit the images to non-commercial research and educational use.
+The DDColor code and weights are released under Apache-2.0. As with FFHQ above, whether the dataset's terms carry over to the trained weights is legally unsettled.
+
 ## Attribution and citations
 
 - GFPGAN: Xintao Wang, Yu Li, Honglun Zhang, Ying Shan. *Towards Real-World Blind Face Restoration with Generative Facial Prior.* CVPR 2021. https://github.com/TencentARC/GFPGAN (Apache-2.0)
 - Real-ESRGAN: Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan. *Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.* ICCVW 2021. https://github.com/xinntao/Real-ESRGAN (BSD-3-Clause)
 - U²-Net: Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R. Zaiane, Martin Jagersand. *U²-Net: Going Deeper with Nested U-Structure for Salient Object Detection.* Pattern Recognition 2020. https://github.com/xuebinqin/U-2-Net (Apache-2.0)
+- DDColor: Xiaoyang Kang, Tao Yang, Wenqi Ouyang, Peiran Ren, Lingzhi Li, Xuansong Xie. *DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders.* ICCV 2023. https://github.com/piddnad/DDColor (Apache-2.0)
 - ONNX conversions: rembg by Daniel Gatis (MIT), sidkonduru/gfpgan-v1.4-onnx, vlad-grig/realesr-general-x4v3-onnx.
 
 See `LICENSE` for the full licence texts.

ddcolor-paper-tiny.onnx

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