{"id":1756,"url":"https://github.com/crypdick/awesome-neural-art","name":"awesome-neural-art","description":"A curated list of awesome neural network-based art resources.","projects_count":37,"last_synced_at":"2026-10-04T05:00:36.250Z","repository":{"id":46297009,"uuid":"157037889","full_name":"crypdick/awesome-neural-art","owner":"crypdick","description":"A curated list of awesome neural network-based art resources.","archived":false,"fork":false,"pushed_at":"2026-05-05T03:08:52.000Z","size":79,"stargazers_count":87,"open_issues_count":0,"forks_count":6,"subscribers_count":3,"default_branch":"master","last_synced_at":"2026-09-14T09:37:16.235Z","etag":null,"topics":["awesome","awesome-list","image-transfer","neural-art","neural-doodles","neural-network","style-transfer","texture-nets"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/crypdick.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2018-11-11T01:41:24.000Z","updated_at":"2026-07-26T19:26:20.000Z","dependencies_parsed_at":"2026-06-12T04:00:30.843Z","dependency_job_id":null,"html_url":"https://github.com/crypdick/awesome-neural-art","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/crypdick/awesome-neural-art","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/crypdick%2Fawesome-neural-art","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/crypdick%2Fawesome-neural-art/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/crypdick%2Fawesome-neural-art/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/crypdick%2Fawesome-neural-art/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/crypdick","download_url":"https://codeload.github.com/crypdick/awesome-neural-art/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/crypdick%2Fawesome-neural-art/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":343072799,"owners_count":37993977,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-10-03T21:59:58.778Z","status":"online","status_checked_at":"2026-10-04T02:00:06.888Z","response_time":127,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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"}},"created_at":"2024-01-04T18:14:31.995Z","updated_at":"2026-10-04T05:00:36.251Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Background subtraction","Lectures","Super Resolution","Style Transfer","Patching","Deep Dream","Colorization","Interactive art","Multiple categories","License"],"sub_categories":[],"readme":"# Awesome Neural Art [![Awesome](https://awesome.re/badge-flat2.svg)](https://awesome.re)\n\n_Update, May 2026:_ I started this repo in 2018. Since then, neural art has come a very long way. I'm keeping this page around as a time capsule of the early days of neural art.\n\nCreating art and manipulating images using deep neural networks.\n\n## Contents\n\n- [Colorization](#colorization)\n- [Style Transfer](#style-transfer)\n- [Super Resolution](#super-resolution)\n- [Patching](#patching)\n- [Background subtraction](#background-subtraction)\n- [Deep Dream](#deep-dream)\n- [Interactive art](#interactive-art)\n- [Multiple categories](#multiple-categories)\n- [Lectures](#lectures)\n\n## Colorization\n\nSee also: [Awesome Image Colorization](https://github.com/MarkMoHR/Awesome-Image-Colorization) and the [colorization subreddit](https://www.reddit.com/r/Colorization) where people do this manually.\n\n- [Interactive Deep Colorization](https://github.com/junyanz/interactive-deep-colorization)\n\n\u003cimg src='https://github.com/junyanz/interactive-deep-colorization/blob/master/imgs/demo.gif' width=600\u003e  \n\n\u003cimg src='https://richzhang.github.io/ideepcolor/index_files/imagenet_showcase_small.jpg' width=800\u003e\n\n- [colornet](https://github.com/pavelgonchar/colornet)\n\n\u003cimg src='https://raw.githubusercontent.com/pavelgonchar/colornet/master/summary/209000_0.png' width=600\u003e  \n\n- [Colorful Image Colorization](https://github.com/richzhang/colorization)\n\n\u003cimg src='https://camo.githubusercontent.com/0f54d76e1561911ef2c423251c386a9368551365/687474703a2f2f726963687a68616e672e6769746875622e696f2f636f6c6f72697a6174696f6e2f7265736f75726365732f696d616765732f746561736572342e6a7067' width=600\u003e  \n\n## Style Transfer\n\n- [neural-style](https://github.com/jcjohnson/neural-style) - Torch implementation of neural style algorithm.\n\n\u003cimg src='https://raw.githubusercontent.com/jcjohnson/neural-style/master/examples/outputs/starry_stanford_bigger.png' width=600\u003e  \n\n- [adaptive-style-transfer](https://github.com/tensorlayer/adaptive-style-transfer) - Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.\n- [deep-photo-styletransfer](https://github.com/luanfujun/deep-photo-styletransfer) - Code and data for [\"Deep Photo Style Transfer\"](https://arxiv.org/abs/1703.07511).\n- [fast-style-transfer](https://github.com/lengstrom/fast-style-transfer#image-stylization) - TensorFlow CNN for fast style transfer with larger scale style features in transformations.\n- [texture_nets](https://github.com/DmitryUlyanov/texture_nets) - Code for \"Texture Networks: Feed-forward Synthesis of Textures and Stylized Images\". \n- [AdaIN-style](https://github.com/xunhuang1995/AdaIN-style) - Code for [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://arxiv.org/abs/1703.06868). Contains neat features such as transfering style without color, style interpolation, and spatial control.\n\n\u003cimg src='https://raw.githubusercontent.com/xunhuang1995/AdaIN-style/master/examples/style_interp.jpg' width=600\u003e  \n\u003cimg src='https://raw.githubusercontent.com/xunhuang1995/AdaIN-style/master/examples/spatial_control.jpg' width=600\u003e  \n\n- [Bicycle GAN](https://github.com/junyanz/BicycleGAN) - NIPS 2017 Toward Multimodal Image-to-Image Translation, PyTorch implementation. Given the same night image, model is able to synthesize possible day images with different types of lighting, sky and clouds.\n\n\u003cimg src='https://github.com/junyanz/BicycleGAN/blob/master/imgs/results_matrix.jpg' width=820\u003e  \n\n- [Neural Style Transfer](https://github.com/titu1994/Neural-Style-Transfer) - Keras Implementation of Neural Style Transfer from the paper [\"A Neural Algorithm of Artistic Style\"](http://arxiv.org/abs/1508.06576) in Keras 2.0+.\n- [Deep Painterly Harmonization](https://github.com/luanfujun/deep-painterly-harmonization) - Photoshopping an object into a painting, and then neural network changes the style (pallete, strokes, luminosity, etc) of the object to match the painting style. [See paper](https://arxiv.org/abs/1804.03189).\n\n## Super Resolution\n\n- [waifu2x](https://github.com/nagadomi/waifu2x) - Image Super-Resolution for Anime-Style Art.\n\n\u003cimg src='https://raw.githubusercontent.com/nagadomi/waifu2x/master/images/slide.png' width=820\u003e  \n\n- [srgan](https://github.com/tensorlayer/srgan) - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.\n\n\u003cimg src='https://raw.githubusercontent.com/tensorlayer/srgan/master/img/SRGAN_Result3.png' width=820\u003e  \n\n- [fft-descreen](https://github.com/6o6o/fft-descreen) - Clean up deconvolution checkerboard artifacts found in style transferred images.\n\n## Patching\n\nAKA in-painting\n\n- [DeepFill V1](https://github.com/JiahuiYu/generative_inpainting) - Generative Image Inpainting with Contextual Attention.\n- [EdgeConnect](https://github.com/knazeri/edge-connect) - a two-stage adversarial model that comprises of an edge generator followed by an image completion network.\n\u003cimg src='https://user-images.githubusercontent.com/1743048/50673917-aac15080-0faf-11e9-9100-ef10864087c8.png' width=820\u003e\n- [Deep Image Completion](https://github.com/adamstseng/general-deep-image-completion) - Contains face-completion and general image completion models. Each can complete images with differnet types of corrupted masks like scribbles, lines, dots and texts.\n\u003cimg src='https://raw.githubusercontent.com/adamstseng/general-deep-image-completion/master/general-completion.jpg' width=820\u003e\n\n\n\n## Background subtraction\n\nAKA image matting. See also: [Awesome Background Subtraction](https://github.com/murari023/awesome-background-subtraction)\n\n- Deep Image Matting - Implementations of the [Deep Image Matting paper](https://sites.google.com/view/deepimagematting).\n  - [Dataset generator](https://github.com/hector-sab/DIM_DataCreation) - Generates training data for the Deep Image Matting paper.\n  - [Tensorflow implementation](https://github.com/Joker316701882/Deep-Image-Matting) - Original paper author.\n  - [PyTorch implementation](https://github.com/foamliu/Deep-Image-Matting-v2) - Same author as [Keras implementation](https://github.com/foamliu/Deep-Image-Matting), but this is their improved (and maintained) codebase.\n  - [Another PyTorch implementation](https://github.com/huochaitiantang/pytorch-deep-image-matting)\n\n- [Salient Object Detection](https://github.com/Joker316701882/Salient-Object-Detection) - Tensorflow implementation for cvpr2017 paper [\"Deeply Supervised Salient Object Detection with Short Connections\"](https://arxiv.org/abs/1611.04849) (not peer reviewed). Same author as original Deep Image Matting author.\n- [AlphaGAN](https://github.com/CDOTAD/AlphaGAN-Matting) - Unofficial implementation of [AlphaGAN: Generative adversarial networks for natural image matting](https://arxiv.org/pdf/1807.10088.pdf). Uses a GAN as the name suggests.\n- [DeepLab V3+](https://github.com/jfzhang95/pytorch-deeplab-xception) - Implementations of an encoder-decoder Modified Aligned Xception and ResNet as backbone [(paper)](https://arxiv.org/pdf/1802.02611.pdf).\n\n## Deep Dream\n\n- [deepdream](https://github.com/google/deepdream) - The original Google implementation.\n\n## Interactive art\n\n- [neural-doodle](https://github.com/alexjc/neural-doodle) - \nTurn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.) \n\n\u003cimg src='https://github.com/alexjc/neural-doodle/blob/master/docs/Workflow.gif' width=820\u003e  \n\n\n- [iGAN](https://github.com/junyanz/iGAN) - iGAN: Interactive Image Generation via Generative Adversarial Networks.\n\n\u003cimg src='https://raw.githubusercontent.com/junyanz/iGAN/master/pics/demo.gif' width=820\u003e  \n\n- [style2paints](https://github.com/lllyasviel/style2paints/) - An AI-driven interactive line art colorization tool. Backend is a Residual U-net and Auxiliary Classifier GAN.\n\n## Multiple categories\n\n- [deep-image-prior](https://github.com/DmitryUlyanov/deep-image-prior) -  Image restoration with neural networks but without learning. Does artifact removal, inpainting, super-resolution, denoising.\n\n## Lectures\n\n- [The Neural Aesthetic @ ITP-NYU, Fall 2018](https://ml4a.github.io/classes/itp-F18/)\n- [Neural Style Transfer Review](https://github.com/ycjing/Neural-Style-Transfer-Papers) - Repo for the [Neural Style Transfer review article](https://arxiv.org/abs/1705.04058).\n\n## License\n\n[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)\n\nTo the extent possible under law, [Richard Decal](https://www.richarddecal.com) has waived all copyright and related or neighboring rights to this work.\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/crypdick%2Fawesome-neural-art/projects"}