# style2paints **Repository Path**: bit2atom/style2paints ## Basic Information - **Project Name**: style2paints - **Description**: sketch + style = paints ! - **Primary Language**: JavaScript - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-08-25 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # STYLE2PAINTS [ZhiHu](https://zhuanlan.zhihu.com/p/29331219) [BiliBili](https://www.bilibili.com/video/av14443094/) The AI can paint on a sketch accroding to a given specific color style. ![web_preview](https://raw.githubusercontent.com/lllyasviel/style2paints/master/page/screen_shot.png) # Example 1 (Google Search results test) A content sketch (**the first google image search result of key word 'anime sketch'**) and some style images: . . . Results: . . . . . . . . . . . . # Example 2 (western sketch) A western content sketch and 2 style images: . . . . # Example 3 (messy sketch) A messy content sketch and 2 style images: . . . . . . . # Example 4 (detailed sketch) A detailed content sketch and 2 style images: . . . . . . . # Example 5 (simple sketch) A simple content sketch **without shadow rendering** and 2 style images: . . . . . . . # Requirement pip install tensorflow_gpu pip install keras pip install chainer pip install cupy pip install bottle pip install h5py pip install opencv-python # Launch Server git clone https://github.com/lllyasviel/style2paints.git (then download all pretrained models from 'release' page and then put them in 'style2paints/server') cd style2paints/server python server.py # Model Models are avaliable in 'release' page. 1. base_generator.net all rights reserved 2017 style2paints 2. paintschainer.net from [paintschainer](https://github.com/pfnet/PaintsChainer) 3. google_net.net from [nico-opendata](https://nico-opendata.jp/en/demo/tag/index.html) # Training Datasets 1. The recommended training dataset of illustrations is the 400k images from [nico-opendata](https://nico-opendata.jp/en/seigadata/index.html) 2. The recommended training sketches is from [sketchKeras](https://github.com/lllyasviel/sketchKeras) # Community QQ Group ID: 184467946 # Paper Reference The paper is accecped by ACPR 2017. @article{StyleTansferForAnime, Author = {Lvmin Zhang and Yi Ji and Xin Lin}, Title = {Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN}, Journal = {arXiv:1706.03319}, Year = {2017} }