{"id":13517475,"url":"https://github.com/UserUnknownFactor/GIMP3-ML","last_synced_at":"2025-03-31T08:31:41.197Z","repository":{"id":58214861,"uuid":"529943999","full_name":"UserUnknownFactor/GIMP3-ML","owner":"UserUnknownFactor","description":"GIMP3 machine learning playground","archived":false,"fork":true,"pushed_at":"2024-11-28T16:17:05.000Z","size":994,"stargazers_count":38,"open_issues_count":1,"forks_count":5,"subscribers_count":1,"default_branch":"GIMP3-ML","last_synced_at":"2024-11-28T16:25:16.767Z","etag":null,"topics":["cv2","gimp","gimp-2-99","gimp-3","gimp-plugin","hentai","machine-learning","manga","python3","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"kritiksoman/GIMP-ML","license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/UserUnknownFactor.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2022-08-28T18:20:13.000Z","updated_at":"2024-11-28T16:17:08.000Z","dependencies_parsed_at":"2023-01-17T19:16:12.074Z","dependency_job_id":null,"html_url":"https://github.com/UserUnknownFactor/GIMP3-ML","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UserUnknownFactor%2FGIMP3-ML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UserUnknownFactor%2FGIMP3-ML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UserUnknownFactor%2FGIMP3-ML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UserUnknownFactor%2FGIMP3-ML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/UserUnknownFactor","download_url":"https://codeload.github.com/UserUnknownFactor/GIMP3-ML/tar.gz/refs/heads/GIMP3-ML","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246441626,"owners_count":20778065,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cv2","gimp","gimp-2-99","gimp-3","gimp-plugin","hentai","machine-learning","manga","python3","pytorch"],"created_at":"2024-08-01T05:01:34.152Z","updated_at":"2025-03-31T08:31:40.583Z","avatar_url":"https://github.com/UserUnknownFactor.png","language":"Python","funding_links":[],"categories":[":sparkles: Graphics Editor"],"sub_categories":["Audio Plugins"],"readme":"# GIMP3-ML\n\nMachine Learning plugins for GIMP 3.\n\nForked from the [original version](https://github.com/kritiksoman/GIMP-ML/tree/GIMP3-ML) to improve the user experience in several aspects:\n* Added more models.\n* Models are run with Python 3.10+.\n* Full error text is shown in the GIMP error dailog and in debug console.\n* Additional alpha channel handling in some plugins.\n* Automatic installation for Windows systems.\n* And other smaller improvements.\n\nThe plugins have been tested with GIMP 2.99.12 on the following systems: \u003cbr\u003e\n* Windows 10\n\n# Installation Steps\n1. Install [GIMP3](https://www.gimp.org/downloads/devel/).\n2. Download this repository.\n3. On Windows:\n      * Install [Python 3.10](https://www.python.org/downloads/).\n      * Run `install.cmd` from the unpacked folder.\n4. You should now find the GIMP-ML plugins under Layers → GIMP-ML. \n5. You can download [the weights here](https://drive.google.com/drive/folders/1ko7j1WOJltJcv-goIBNTIGGniZ68kEQa), or from the weight links below.\n\n![Screenshot](screenshot.png)\n\n# References\n### Background Removal\n* Source: https://github.com/danielgatis/rembg\n* Weights: \n    - u2net ([download](https://drive.google.com/uc?id=1tCU5MM1LhRgGou5OpmpjBQbSrYIUoYab), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for general use cases.\n    - u2netp ([download](https://drive.google.com/uc?id=1tNuFmLv0TSNDjYIkjEdeH1IWKQdUA4HR), [source](https://github.com/xuebinqin/U-2-Net)): A lightweight version of u2net model.\n    - u2net_human_seg ([download](https://drive.google.com/uc?id=1ZfqwVxu-1XWC1xU1GHIP-FM_Knd_AX5j), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for human segmentation.\n    - *(unused) u2net_cloth_seg* ([download](https://drive.google.com/uc?id=15rKbQSXQzrKCQurUjZFg8HqzZad8bcyz), [source](https://github.com/levindabhi/cloth-segmentation)): A pre-trained model for Cloths Parsing from human portrait. Here clothes are parsed into 3 category: Upper body, Lower body and Full body.\n* License: MIT License\n\n### Anime-style Inpainting\n* Source: https://github.com/youyuge34/Anime-InPainting\n* Weights: [Google Drive](https://drive.google.com/file/d/12I-K7GQEXEL_rEOVJnRv7ecVHyuZE-1-/view?usp=sharing) | [Baidu](https://pan.baidu.com/s/1WkeRtYViGGGw4fUqPo3nsg)\n* License: [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/)\n```\n@inproceedings{nazeri2019edgeconnect,\n  title={EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning},\n  author={Nazeri, Kamyar and Ng, Eric and Joseph, Tony and Qureshi, Faisal and Ebrahimi, Mehran},\n  journal={arXiv preprint},\n  year={2019}}\n```\n### Demosaics\n* Source: \n  * Demosaics: https://github.com/rekaXua/demosaic_project\n  * ESRGAN: https://github.com/xinntao/ESRGAN\n* Weights: [4x_FatalPixels](https://de-next.owncube.com/index.php/s/mDGmi7NgdyyQRXL/download?path=%2F\u0026files=4x_FatalPixels_340000_G.pth)\n* Licenses: \n  * Demosaics: GNU Affero General Public License v3.0\n  * ESRGAN: Apache-2.0 license \n\u003e [[Paper](https://arxiv.org/abs/2107.10833)] \u003cbr\u003e\n\u003e [Xintao Wang](https://xinntao.github.io/), Liangbin Xie, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ\u0026hl=en) \u003cbr\u003e\n\u003e Applied Research Center (ARC), Tencent PCG\u003cbr\u003e\n\u003e Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences\n\n### Inpainting\n* Source: https://github.com/a-mos/High_Resolution_Image_Inpainting\n* License: [Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/)\n```\n@article{Moskalenko_2020,\n\tdoi = {10.51130/graphicon-2020-2-4-18},\n\turl = {https://doi.org/10.51130%2Fgraphicon-2020-2-4-18},\n\tyear = 2020,\n\tmonth = {dec},\n\tpages = {short18--1--short18--9},\n\tauthor = {Andrey Moskalenko and Mikhail Erofeev and Dmitriy Vatolin},\n\ttitle = {Deep Two-Stage High-Resolution Image Inpainting},\n\tjournal = {Proceedings of the 30th International Conference on Computer Graphics and Machine Vision ({GraphiCon} 2020). Part 2}} \n```\n### SRResNet\n* Source: https://github.com/twtygqyy/pytorch-SRResNet\n* Torch Hub fork: https://github.com/valgur/pytorch-SRResNet\n* License: [MIT](https://github.com/twtygqyy/pytorch-SRResNet/blob/master/LICENSE)\n* C. Ledig et al., “[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](http://arxiv.org/abs/1609.04802),”\n  in *2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2017, pp. 105–114.\n\n### Neural Colorization\n* Source: https://github.com/zeruniverse/neural-colorization\n* Torch Hub fork: https://github.com/valgur/neural-colorization\n* License:\n   * [GNU GPL 3.0](https://github.com/zeruniverse/neural-colorization/blob/pytorch/LICENSE) for personal or research use\n   * Commercial use prohibited\n   * Model weights released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)\n* Based on fast-neural-style:\n   * https://github.com/jcjohnson/fast-neural-style\n   * License:\n      * Free for personal or research use\n      * For commercial use please contact the authors\n   * J. Johnson, A. Alahi, and L. Fei-Fei, “[Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://cs.stanford.edu/people/jcjohns/papers/eccv16/JohnsonECCV16.pdf),”\n     in *Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*,\n     vol. 9906 LNCS, 2016, pp. 694–711.\n\n### Edge Detection (DexiNed)\n* Source: https://github.com/xavysp/DexiNed\n* Weights: [BIPED](https://drive.google.com/file/d/1V56vGTsu7GYiQouCIKvTWl5UKCZ6yCNu/view?usp=sharing)\n* License: MIT license \n```\n@misc{soria2021dexined_ext,\n    title={Dense Extreme Inception Network for Edge Detection},\n    author={Xavier Soria and Angel Sappa and Patricio Humanante and Arash Arbarinia},\n    year={2021},\n    eprint={arXiv:2112.02250},\n    archivePrefix={arXiv},\n    primaryClass={cs.CV}}\n```\n### DeblurGANv2\n* Source: https://github.com/TAMU-VITA/DeblurGANv2\n* Torch Hub fork: https://github.com/valgur/DeblurGANv2\n* License: [BSD 3-clause](https://github.com/TAMU-VITA/DeblurGANv2/blob/master/LICENSE)\n* O. Kupyn, T. Martyniuk, J. Wu, and Z. Wang, “[DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better](https://arxiv.org/abs/1908.03826),”\n  in *2019 IEEE/CVF International Conference on Computer Vision (ICCV)*, 2019, pp. 8877–8886.\n\n### Monodepth2\n* Source: https://github.com/nianticlabs/monodepth2\n* Torch Hub fork: https://github.com/valgur/monodepth2\n* License:\n   * See the [license file](https://github.com/nianticlabs/monodepth2/blob/master/LICENSE) for terms\n   * Copyright © Niantic, Inc. 2019. Patent Pending. All rights reserved.\n   * Non-commercial use only\n* C. Godard, O. Mac Aodha, M. Firman, and G. Brostow, “[Digging Into Self-Supervised Monocular Depth Estimation](http://arxiv.org/abs/1806.01260),”\n  in *2019 IEEE/CVF International Conference on Computer Vision (ICCV)*, 2019, pp. 3827–3837.\n\n# Authors\n* UserUnknownFactor\n* Kritik Soman ([kritiksoman](https://github.com/kritiksoman)) – original GIMP-ML implementation\n\n# License\nMIT\n\nPlease note that additional license terms apply for each individual model. See the [references](#references) list for details.\nMany of the models restrict usage to non-commercial or research purposes only.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FUserUnknownFactor%2FGIMP3-ML","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FUserUnknownFactor%2FGIMP3-ML","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FUserUnknownFactor%2FGIMP3-ML/lists"}