The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

Last update: Nov 21, 2022

Related tags

Deep Learning ASMAGAN

Overview

ASMA-GAN

Anisotropic Stroke Control for Multiple Artists Style Transfer

Proceedings of the 28th ACM International Conference on Multimedia

The official repository with Pytorch

[Arxiv paper]

Methodology

Dependencies

python3.6+
pytorch1.5+
torchvision
pyyaml
paramiko
pandas
requests
tensorboard
tensorboardX
tqdm

Installation

We highly recommend you to use Anaconda for installation

conda create -n ASMA python=3.6
conda activate ASMA
conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.1 -c pytorch
pip install pyyaml paramiko pandas requests tensorboard tensorboardX tqdm

Preparation

Traning dataset
- Coming soon
pre-trained model
- Download the model from Github Releases, and unzip the files to ./train_logs/

Usage

To test with pretrained model

The command line below will generate 1088*1920 HD style migration pictures of 11 painters for each picture of testImgRoot (11 painters include: Berthe Moriso , Edvard Munch, Ernst Ludwig Kirchner, Jackson Pollock, Wassily Kandinsky, Oscar-Claude Monet, Nicholas Roerich, Paul Cézanne, Pablo Picasso ,Samuel Colman, Vincent Willem van Gogh. The output image(s) can be found in ./test_logs/ASMAfinal/

Example of style transfer with all 11 artists style

python main.py --mode test --cuda 0 --version ASMAfinal  --dataloader_workers 8   --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle -1

Example of style transfer with Pablo Picasso style

python main.py --mode test --cuda 0 --version ASMAfinal  --dataloader_workers 8   --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 8

Example of style transfer with Wassily Kandinsky style

python main.py --mode test --cuda 0 --version ASMAfinal  --dataloader_workers 8   --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 4

--version refers to the ASMAGAN training logs name.

--testImgRoot can be a folder with images or the path of a single picture.You can assign the image(s) you want to perform style transfer to this argument.

--specify_sytle is used to specify which painter's style is used for style transfer. When the value is -1, 11 painters' styles are used for image(s) respectively for style transfer. The values corresponding to each painter's style are as follows [0: Berthe Moriso, 1: Edvard Munch, 2: Ernst Ludwig Kirchner, 3: Jackson Pollock, 4: Wassily Kandinsky, 5: Oscar-Claude Monet, 6: Nicholas Roerich, 7: Paul Cézanne, 8: Pablo Picasso, 9 : Samuel Colman, 10: Vincent Willem van Gogh]

Training

Coming soon

To cite our paper

@inproceedings{DBLP:conf/mm/ChenYLQN20,
  author    = {Xuanhong Chen and
               Xirui Yan and
               Naiyuan Liu and
               Ting Qiu and
               Bingbing Ni},
  title     = {Anisotropic Stroke Control for Multiple Artists Style Transfer},
  booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia, 2020},
  publisher = {{ACM}},
  year      = {2020},
  url       = {https://doi.org/10.1145/3394171.3413770},
  doi       = {10.1145/3394171.3413770},
  timestamp = {Thu, 15 Oct 2020 16:32:08 +0200},
  biburl    = {https://dblp.org/rec/conf/mm/ChenYLQN20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Some Results

Related Projects

Learn about our other projects [RainNet], [Sketch Generation], [CooGAN], [Knowledge Style Transfer], [SimSwap],[ASMA-GAN],[Pretrained_VGG19].

High Resolution Results

Comments

Can't download pre-trained model

Hi! Could you please check your pre-trained model. The follow links is no found. Thank you https://github.com/neuralchen/ASMAGAN/releases/download/v.1.0/ASMAfinal.zip

opened by namdn 5
Thank you for your great project. When will the training code be released
Thank you for your great project.

When will the training code be released.

I want to get more painters how do I do that, how do I make the training datasets, how much data do I need

Looking forward to your reply
opened by zhanghongyong123456 5
Fine Tuning for single class

Hello team, I would like to finetune your pretrained model for just five new class (total output will be five), how should I use the finetune? Thank you!

opened by minhtcai 0
KeyError 1920

using the official command: python main.py --mode test --cuda 0 --version ASMAfinal --dataloader_workers 8 --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 8

then error happened Generator Script Name: Conditional_Generator_asm 11 classes Finished preprocessing the test dataset, total image number: 25... /home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/transforms.py:332: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. warnings.warn( Traceback (most recent call last): File "/home/ama/ASMAGAN/main.py", line 266, in tester.test() File "/home/ama/ASMAGAN/test_scripts/tester_common_useage.py", line 50, in test test_data = TestDataset(test_img,batch_size) File "/home/ama/ASMAGAN/data_tools/test_data_loader_resize.py", line 36, in init transform.append(T.Resize(1088,1920)) File "/home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/transforms.py", line 336, in init interpolation = _interpolation_modes_from_int(interpolation) File "/home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/functional.py", line 47, in _interpolation_modes_from_int return inverse_modes_mapping[i] KeyError: 1920

opened by Kayce001 1
Change aspect ratio of images

test code change aspect ratio of input images so output images are deformed to fix this i make some correction at "test_data_loader_resize.py"

opened by birolkuyumcu 0
RuntimeError: cuDNN

Hi I get the following error when running the code:

RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED when calling backward()

I would appreciate your help on how to resolve this.

Thank you!

Gero

opened by Limbicnation 8

Releases(v.1.1)

v.1.1(Jun 14, 2021)

ASMA pretrained model
Source code(tar.gz)
Source code(zip)
ASMAfinal.zip(194.71 MB)
data_art_backup.zip(1427.14 MB)

Owner

Six_God

GitHub Repository

Code for the CVPR 2021 paper "Triple-cooperative Video Shadow Detection"

Triple-cooperative Video Shadow Detection Code and dataset for the CVPR 2021 paper "Triple-cooperative Video Shadow Detection"[arXiv link] [official l

24 Oct 04, 2022

Code for Understanding Pooling in Graph Neural Networks

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

37 Dec 13, 2022

A Jinja extension (compatible with Flask and other frameworks) to compile and/or compress your assets.

94 Nov 21, 2022

A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

36 Dec 29, 2022

Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)

Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU Implementation of CVPR 2020 paper: Bundle Adjustment o

34 Dec 05, 2022

A simple API wrapper for Discord interactions.

Your ultimate Discord interactions library for discord.py. About | Installation | Examples | Discord | PyPI About What is discord-py-interactions? dis

641 Jan 03, 2023

A distributed deep learning framework that supports flexible parallelization strategies.

FlexFlow FlexFlow is a deep learning framework that accelerates distributed DNN training by automatically searching for efficient parallelization stra

528 Dec 25, 2022

NAS-FCOS: Fast Neural Architecture Search for Object Detection (CVPR 2020)

NAS-FCOS: Fast Neural Architecture Search for Object Detection This project hosts the train and inference code with pretrained model for implementing

180 Dec 06, 2022

Metrics to evaluate quality and efficacy of synthetic datasets.

An Open Source Project from the Data to AI Lab, at MIT Metrics for Synthetic Data Generation Projects Website: https://sdv.dev Documentation: https://

129 Jan 03, 2023

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning. Users or developers can query Unicorn for a performance task.

27 Jan 05, 2023

This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (EMNLP 2020)

Towards Persona-Based Empathetic Conversational Models (PEC) This is the repo for our work "Towards Persona-Based Empathetic Conversational Models" (E

35 Nov 17, 2022

Trajectory Extraction of road users via Traffic Camera

Traffic Monitoring Citation The associated paper for this project will be published here as soon as possible. When using this software, please cite th

14 Dec 17, 2022

EdiBERT is a generative model based on a bi-directional transformer, suited for image manipulation

EdiBERT, a generative model for image editing EdiBERT is a generative model based on a bi-directional transformer, suited for image manipulation. The

16 Dec 07, 2022

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR)

3k Dec 31, 2022