README.md

metadata

license: other
license_name: mixed-apache-2.0-bsd-3-clause
license_link: LICENSE
library_name: onnx
pipeline_tag: image-to-image
tags:
  - onnx
  - gfpgan
  - real-esrgan
  - u2net
  - face-restoration
  - super-resolution
  - background-removal
  - colorization
  - ddcolor
  - on-device

OptiMix models

ONNX model files downloaded on first use by the OptiMix Android app (com.momoapps.optimix, MomoApps). Every file is a mirror or a derivative of third-party open-source weights. All credit goes to the original authors listed below. Each file keeps its upstream licence. This repository adds no new licence terms.

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Files

File	Size (bytes)	SHA-256	Model	Upstream source	Licence
gfpgan-v1.4-int8.onnx	85,567,123	9771b4b4eb9568d4458bbee4ebb59ef9380827a6f91a5814603b08bf356db91b	GFPGAN v1.4, weight-only INT8 (default enhancer)	Built by MomoApps from the official GFPGANv1.4.pth (TencentARC/GFPGAN release v1.3.0, params_ema) exported with the clean GFPGANv1Clean architecture (tag v1.3.4), then per-channel INT8-quantised (weights only, including the StyleGAN modulated-conv kernels).	Apache-2.0, © TencentARC (Xintao Wang et al.)
gfpgan-v1.4.onnx	170,362,246	6b12e9428c2a0983b2726604f09c4eec9e2e250370c79946a31b139f9befc1ea	GFPGAN v1.4, fp16 weights / fp32 I/O (HD option)	Unmodified copy of gfpgan14-fp16.onnx from sidkonduru/gfpgan-v1.4-onnx (commit 3777aaa98f90f704884e977670d8abfc15ede03f), a conversion of the official TencentARC GFPGANv1.4.pth	Apache-2.0, © TencentARC (Xintao Wang et al.)
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 (commit 0e95d93e454cf6b5de9bdf5a1c9e7ec340d38231), an export of the official realesr-general-x4v3.pth from xinntao/Real-ESRGAN release 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 (rembg is MIT), converted from the weights of 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 (rembg is MIT), converted from the weights of 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 (commit cf9fd99c1d7472689ec7413441c1b799a51866a3, pytorch_model.bin sha256 8a1277bc90a1bfbb6d2d83933a9a6bc821931879ca93e26e4fcec12165d41fce) and the architecture in 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)
ddcolor-paper-tiny-int8.onnx	56,619,279	29f3a5f5f794959faebaf034a142c34712833623f9ce672f6ef8f0a8ae8e5f5a	DDColor-T, weight-only per-channel INT8 (default colouriser)	Built by MomoApps from ddcolor-paper-tiny.onnx above with scripts/quantize_ddcolor_int8.py. The 161 Conv/MatMul/Gemm weight tensors are stored as symmetric INT8 with one scale per output channel and expanded by DequantizeLinear. All compute stays float32, and Einsum, Erf, LayerNormalization, norms and biases stay in float. Against the float32 ONNX on two DDColor test photos, the mean ab difference is 0.41 / 0.32. Visually indistinguishable apart from edge artifacts the float model already has.	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 (both files): 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. The INT8 file also uses DequantizeLinear (opset 13+, per-axis). ONNX Runtime's mobile checker recommends the CPU execution provider for both files, and both convert to .ort format.
• 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.

Build scripts (DDColor)

scripts/ holds the scripts that produced the DDColor files. They are deterministic: re-running them on the box gave byte-identical files.

• export_ddcolor_onnx.py DDCOLOR_REPO WEIGHTS_DIR OUT.onnx TEST_IMAGE [PREVIEW.jpg] exports the float32 ONNX (opset 17) from the official weights. It folds spectral norm into plain weights and checks the result against PyTorch: max |Δab| was 0.0022 on the Audrey Hepburn test photo.
• quantize_ddcolor_int8.py SRC.onnx DEST.onnx produces the weight-only per-channel INT8 file.
• compare_ddcolor.py REF.onnx TEST.onnx OUTDIR IMG... reports the ab differences and writes side-by-side colourised JPGs.

The tools used were torch 2.15.0 (CPU), onnx 1.23.2, onnxruntime 1.31.1 and opencv-python-headless.

Training data note: FFHQ

GFPGAN was trained on the FFHQ face dataset. FFHQ's images are published under a non-commercial dataset licence (Creative Commons BY-NC-SA 4.0 for the dataset compilation, plus per-image licences). 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 (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.