Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

Overview

Stylized Neural Painting

Open in RunwayML Badge

Preprint | Project Page | Colab Runtime 1 | Colab Runtime 2

Official PyTorch implementation of the preprint paper "Stylized Neural Painting", accepted to CVPR 2021.

We propose an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering. Since a typical vector render is not differentiable, we design a novel neural renderer which imitates the behavior of the vector renderer and then frame the stroke prediction as a parameter searching process that maximizes the similarity between the input and the rendering output. Experiments show that the paintings generated by our method have a high degree of fidelity in both global appearance and local textures. Our method can be also jointly optimized with neural style transfer that further transfers visual style from other images.

In this repository, we implement the complete training/inference pipeline of our paper based on Pytorch and provide several demos that can be used for reproducing the results reported in our paper. With the code, you can also try on your own data by following the instructions below.

The implementation of the sinkhorn loss in our code is partially adapted from the project SinkhornAutoDiff.

License

Creative Commons License Stylized Neural Painting by Zhengxia Zou is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

One-min video result

IMAGE ALT TEXT HERE

**Updates on CPU mode (Nov 29, 2020)

PyTorch-CPU mode is now supported! You can try out on your local machine without any GPU cards.

**Updates on lightweight renderers (Nov 26, 2020)

We have provided some lightweight renderers where users now can easily generate high resolution paintings with much more stroke details. With the lightweight renders, the rendering speed also improves a lot (x3 faster). This update also solves the out-of-memory problem when running our demo on a GPU card with limited memory (e.g. 4GB).

Please check out the following for more details.

Requirements

See Requirements.txt.

Setup

  1. Clone this repo:
git clone https://github.com/jiupinjia/stylized-neural-painting.git 
cd stylized-neural-painting
  1. Download one of the pretrained neural renderers from Google Drive (1. oil-paint brush, 2. watercolor ink, 3. marker pen, 4. color tapes), and unzip them to the repo directory.
unzip checkpoints_G_oilpaintbrush.zip
unzip checkpoints_G_rectangle.zip
unzip checkpoints_G_markerpen.zip
unzip checkpoints_G_watercolor.zip
  1. We have also provided some lightweight renderers where users can generate high-resolution paintings on their local machine with limited GPU memory. Please feel free to download and unzip them to your repo directory. (1. oil-paint brush (lightweight), 2. watercolor ink (lightweight), 3. marker pen (lightweight), 4. color tapes (lightweight)).
unzip checkpoints_G_oilpaintbrush_light.zip
unzip checkpoints_G_rectangle_light.zip
unzip checkpoints_G_markerpen_light.zip
unzip checkpoints_G_watercolor_light.zip

To produce our results

Photo to oil painting

  • Progressive rendering
python demo_prog.py --img_path ./test_images/apple.jpg --canvas_color 'white' --max_m_strokes 500 --max_divide 5 --renderer oilpaintbrush --renderer_checkpoint_dir checkpoints_G_oilpaintbrush --net_G zou-fusion-net
  • Progressive rendering with lightweight renderer (with lower GPU memory consumption and faster speed)
python demo_prog.py --img_path ./test_images/apple.jpg --canvas_color 'white' --max_m_strokes 500 --max_divide 5 --renderer oilpaintbrush --renderer_checkpoint_dir checkpoints_G_oilpaintbrush_light --net_G zou-fusion-net-light
  • Rendering directly from mxm image grids
python demo.py --img_path ./test_images/apple.jpg --canvas_color 'white' --max_m_strokes 500 --m_grid 5 --renderer oilpaintbrush --renderer_checkpoint_dir checkpoints_G_oilpaintbrush --net_G zou-fusion-net

Photo to marker-pen painting

  • Progressive rendering
python demo_prog.py --img_path ./test_images/diamond.jpg --canvas_color 'black' --max_m_strokes 500 --max_divide 5 --renderer markerpen --renderer_checkpoint_dir checkpoints_G_markerpen --net_G zou-fusion-net
  • Progressive rendering with lightweight renderer (with lower GPU memory consumption and faster speed)
python demo_prog.py --img_path ./test_images/diamond.jpg --canvas_color 'black' --max_m_strokes 500 --max_divide 5 --renderer markerpen --renderer_checkpoint_dir checkpoints_G_markerpen_light --net_G zou-fusion-net-light
  • Rendering directly from mxm image grids
python demo.py --img_path ./test_images/diamond.jpg --canvas_color 'black' --max_m_strokes 500 --m_grid 5 --renderer markerpen --renderer_checkpoint_dir checkpoints_G_markerpen --net_G zou-fusion-net

Style transfer

  • First, you need to generate painting and save stroke parameters to output dir
python demo.py --img_path ./test_images/sunflowers.jpg --canvas_color 'white' --max_m_strokes 500 --m_grid 5 --renderer oilpaintbrush --renderer_checkpoint_dir checkpoints_G_oilpaintbrush --net_G zou-fusion-net --output_dir ./output
  • Then, choose a style image and run style transfer on the generated stroke parameters
python demo_nst.py --renderer oilpaintbrush --vector_file ./output/sunflowers_strokes.npz --style_img_path ./style_images/fire.jpg --content_img_path ./test_images/sunflowers.jpg --canvas_color 'white' --net_G zou-fusion-net --renderer_checkpoint_dir checkpoints_G_oilpaintbrush --transfer_mode 1

You may also specify the --transfer_mode (0: transfer color only, 1: transfer both color and texture)

Also, please note that in the current version, the style transfer are not supported by the progressive rendering mode. We will be working on this feature in the near future.

Generate 8-bit graphic artworks

python demo_8bitart.py --img_path ./test_images/monalisa.jpg --canvas_color 'black' --max_m_strokes 300 --max_divide 4

Running through SSH

If you would like to run remotely through ssh and do not have something like X-display installed, you will need --disable_preview to turn off cv2.imshow on the run.

python demo_prog.py --disable_preview

Google Colab

Here we also provide a minimal working example of the inference runtime of our method. Check out the following runtimes and see your result on Colab.

Colab Runtime 1 : Image to painting translation (progressive rendering)

Colab Runtime 2 : Image to painting translation with image style transfer

To retrain your neural renderer

You can also choose a brush type and train the stroke renderer from scratch. The only thing to do is to run the following common. During the training, the ground truth strokes are generated on-the-fly, so you don't need to download any external dataset.

python train_imitator.py --renderer oilpaintbrush --net_G zou-fusion-net --checkpoint_dir ./checkpoints_G --vis_dir val_out --max_num_epochs 400 --lr 2e-4 --batch_size 64

Citation

If you use our code for your research, please cite the following paper:

@inproceedings{zou2020stylized,
    title={Stylized Neural Painting},
      author={Zhengxia Zou and Tianyang Shi and Shuang Qiu and Yi Yuan and Zhenwei Shi},
      year={2020},
      eprint={2011.08114},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Owner
Zhengxia Zou
Postdoc at the University of Michigan. Research interest: computer vision and applications in remote sensing, self-driving, and video games.
Zhengxia Zou
Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification

This repo holds the codes of our paper: Adaptive Dropblock Enhanced GenerativeAdversarial Networks for Hyperspectral Image Classification, which is ac

Feng Gao 17 Dec 28, 2022
Hydra: an Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems

Hydra: An Extensible Fuzzing Framework for Finding Semantic Bugs in File Systems Paper Finding Semantic Bugs in File Systems with an Extensible Fuzzin

gts3.org (<a href=[email protected])"> 129 Dec 15, 2022
Code Repo for the ACL21 paper "Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning"

Common Sense Beyond English: Evaluating and Improving Multilingual LMs for Commonsense Reasoning This is the Github repository of our paper, "Common S

INK Lab @ USC 19 Nov 30, 2022
The Hailo Model Zoo includes pre-trained models and a full building and evaluation environment

Hailo Model Zoo The Hailo Model Zoo provides pre-trained models for high-performance deep learning applications. Using the Hailo Model Zoo you can mea

Hailo 50 Dec 07, 2022
Used to record WKU's utility bills on a regular basis.

WKU水电费小助手 一个用于定期记录WKU水电费的脚本 Looking for English Readme? 背景 由于WKU校园内的水电账单系统时常存在扣费延迟的现象,而补扣的费用缺乏令人信服的证明。不少学生为费用摸不着头脑,但也没有申诉的依据。为了更好地掌握水电费使用情况,留下一手证据,我开源

2 Jul 21, 2022
RepVGG: Making VGG-style ConvNets Great Again

RepVGG: Making VGG-style ConvNets Great Again (PyTorch) This is a super simple ConvNet architecture that achieves over 80% top-1 accuracy on ImageNet

2.8k Jan 04, 2023
Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer

ConSERT Code for our ACL 2021 paper - ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer Requirements torch==1.6.0

Yan Yuanmeng 478 Dec 25, 2022
prior-based-losses-for-medical-image-segmentation

Repository for papers: Benchmark: Effect of Prior-based Losses on Segmentation Performance: A Benchmark Midl: A Surprisingly Effective Perimeter-based

Rosana EL JURDI 9 Sep 07, 2022
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

dm_control: DeepMind Infrastructure for Physics-Based Simulation. DeepMind's software stack for physics-based simulation and Reinforcement Learning en

DeepMind 3k Dec 31, 2022
一个多模态内容理解算法框架,其中包含数据处理、预训练模型、常见模型以及模型加速等模块。

Overview 架构设计 插件介绍 安装使用 框架简介 方便使用,支持多模态,多任务的统一训练框架 能力列表: bert + 分类任务 自定义任务训练(插件注册) 框架设计 框架采用分层的思想组织模型训练流程。 DATA 层负责读取用户数据,根据 field 管理数据。 Parser 层负责转换原

Tencent 265 Dec 22, 2022
Hooks for VCOCO

Verbs in COCO (V-COCO) Dataset This repository hosts the Verbs in COCO (V-COCO) dataset and associated code to evaluate models for the Visual Semantic

Saurabh Gupta 131 Nov 24, 2022
This is a library for training and applying sparse fine-tunings with torch and transformers.

This is a library for training and applying sparse fine-tunings with torch and transformers. Please refer to our paper Composable Sparse Fine-Tuning f

Cambridge Language Technology Lab 37 Dec 30, 2022
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks

Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks Requirements python 0.10+ rdkit 2020.03.3.0 biopython 1.78 openbabel 2.4

Neeraj Kumar 3 Nov 23, 2022
A PyTorch implementation of the paper "Semantic Image Synthesis via Adversarial Learning" in ICCV 2017

Semantic Image Synthesis via Adversarial Learning This is a PyTorch implementation of the paper Semantic Image Synthesis via Adversarial Learning. Req

Seonghyeon Nam 146 Nov 25, 2022
Easy to use Audio Tagging in PyTorch

Audio Classification, Tagging & Sound Event Detection in PyTorch Progress: Fine-tune on audio classification Fine-tune on audio tagging Fine-tune on s

sithu3 15 Dec 22, 2022
Deeper DCGAN with AE stabilization

AEGeAN Deeper DCGAN with AE stabilization Parallel training of generative adversarial network as an autoencoder with dedicated losses for each stage.

Tyler Kvochick 36 Feb 17, 2022
HyperPose is a library for building high-performance custom pose estimation applications.

HyperPose is a library for building high-performance custom pose estimation applications.

TensorLayer Community 1.2k Jan 04, 2023
Use evolutionary algorithms instead of gridsearch in scikit-learn

sklearn-deap Use evolutionary algorithms instead of gridsearch in scikit-learn. This allows you to reduce the time required to find the best parameter

rsteca 709 Jan 03, 2023
An ML & Correlation platform for transforming disparate data points of interest into usable intelligence.

SSIDprobeCollector An ML & Correlation platform for transforming disparate data points of interest into usable intelligence. At a High level the platf

Bill Reyor 1 Jan 30, 2022
Deep Image Matting implementation in PyTorch

Deep Image Matting Deep Image Matting paper implementation in PyTorch. Differences "fc6" is dropped. Indices pooling. "fc6" is clumpy, over 100 millio

Yang Liu 724 Dec 27, 2022