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PhotoCraft
PhotoCraft — a clean-room Photoshop reimplementation in pure Rust: layers, masks, adjustment layers, type, vectors, brushes and real PSD files, offline, MIT / Apache-2.0
★ 73,2 k étoilesMIT / Apache-2.0
Voir le guide →Anime2Sketch est un projet de image open source, créé par Mukosame (Mukosame/Anime2Sketch sur GitHub). Il compte 2 131 étoiles sur GitHub et 172 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 8 septembre 2026.
Extract clean line sketches from anime art, illustrations, and manga with a pretrained GAN model.
Gather the illustrations or images you want converted to sketches, and decide on CPU or GPU inference.
Clone the repository and install the required packages.
Download the pretrained weights (including the artifact-free variant for dark or low-contrast images) into the weights folder.
Run the test script on a file or directory, choosing input size and output directory, and review the extracted sketches.
Ships with downloadable weights ready for inference, including an artifact-free version tuned for dark and low-contrast images.
Designed for illustrations, anime art, and manga, and also demonstrated on hand-drawn photos and freehand sketch cleanup.
One command processes a file or a whole directory, resizing inputs to a configurable load size such as 512.
A Makefile-driven Docker build and run flow mounts your input and output folders, with documented tweaks for CPU-only runs.
Implements the published adversarial open domain adaptation approach, with citations to the WACV 2022 paper.
A community Gradio demo linked from the README lets you try the model without installing anything.
Python 3 with Pillow and PyTorch is required.
git clone https://github.com/Mukosame/Anime2Sketch.git
cd Anime2Sketch
pip install -r requirements.txtFetch the weights from the Google Drive link in the README and place them in the weights folder.
# download weights per README and put them into weights/Process the bundled example or your own images.
python3 test.py --dataroot test_samples/madoka.jpg --load_size 512 --output_dir results/It is maintained by Mukosame (Xiaoyu Xiang) and collaborators, from the research group behind the underlying paper.
Yes, it is MIT-licensed, and the pretrained weights are freely downloadable.
No API key is involved; the model runs locally in PyTorch.
Generic edge detectors find gradients everywhere; Anime2Sketch is a learned model trained to produce clean, human-style sketch lines from artwork and suppress colour and shading noise.
Clone the repo, pip install -r requirements.txt, download the weights into weights/, and run test.py on your image.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.