PaintTransformer Save

Officially unofficial re-implementation of paper: Paint Transformer: Feed Forward Neural Painting with Stroke Prediction, ICCV 2021.

Project README

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

[Paper] [Official Paddle Implementation] [Huggingface Gradio Demo] [Unofficial PyTorch Re-Implementation] [Colab]

Overview

This repository contains the officially unofficial PyTorch re-implementation of paper:

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction,

Songhua Liu*, Tianwei Lin*, Dongliang He, Fu Li, Ruifeng Deng, Xin Li, Errui Ding, Hao Wang (* indicates equal contribution)

ICCV 2021 (Oral)

Prerequisites

  • Linux or macOS
  • Python 3
  • PyTorch 1.7+ and other dependencies (torchvision, visdom, dominate, and other common python libs)

Getting Started

  • Clone this repository:

    git clone https://github.com/Huage001/PaintTransformer
    cd PaintTransformer
    
  • Download pretrained model from Google Drive and move it to inference directory:

    mv [Download Directory]/model.pth inference/
    cd inference
    
  • Inference:

    python inference.py
    
    • Input image path, output path, and etc can be set in the main function.
    • Notably, there is a flag serial as one parameter of the main function:
      • If serial is True, strokes would be rendered serially. The consumption of video memory will be low but it requires more time.
      • If serial is False, strokes would be rendered in parallel. The consumption of video memory will be high but it would be faster.
      • If animated results are required, serial must be True.
  • Train:

    • Before training, start visdom server:

      python -m visdom.server
      
    • Then, simply run:

      cd train
      bash train.sh
      
    • You can monitor training status at http://localhost:8097/ and models would be saved at checkpoints/painter folder.

  • You may feel free to try other training options written in train.sh.

More Results

Input Animated Output

App

Citation

  • If you find ideas or codes useful for your research, please cite:

    @inproceedings{liu2021paint,
      title={Paint Transformer: Feed Forward Neural Painting with Stroke Prediction},
      author={Liu, Songhua and Lin, Tianwei and He, Dongliang and Li, Fu and Deng, Ruifeng and Li, Xin and Ding, Errui and Wang, Hao},
      booktitle={Proceedings of the IEEE International Conference on Computer Vision},
      year={2021}
    }
    

Acknowledgments

Open Source Agenda is not affiliated with "PaintTransformer" Project. README Source: Huage001/PaintTransformer
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