Multiple style transfer via variational autoencoder

Related tags

Deep Learning ST-VAE
Overview

ST-VAE

Multiple style transfer via variational autoencoder

By Zhi-Song Liu, Vicky Kalogeiton and Marie-Paule Cani

This repo only provides simple testing codes, pretrained models and the network strategy demo.

We propose a Multiple style transfer via variational autoencoder (ST-VAE)

Please check our paper or arxiv paper

BibTex

    @InProceedings{Liu2021stvae,
        author = {Zhi-Song Liu and Wan-Chi Siu and Marie-Paule Cani},
        title = {Multiple Style Transfer via Variational AutoEncoder},
        booktitle = {2021 IEEE International Conference on Image Processing(ICIP)},
        month = {Oct},
        year = {2021}
    }

For proposed ST-VAE model, we claim the following points:

• First working on using Variational AutoEncoder for image style transfer.

• Multiple style transfer by proposed VAE based Linear Transformation.

Dependencies

Python > 3.0
Pytorch > 1.0
NVIDIA GPU + CUDA

Complete Architecture

The complete architecture is shown as follows,

network

Visualization

1. Single style transfer

st_single

2. Multiple style transfer

st_multiple

Implementation

1. Quick testing

  1. Download pre-trained models from

https://drive.google.com/file/d/1WZrvjCGBO1mpggkdJiaw8jp-6ywbXn4J/view?usp=sharing

and copy them to the folder "models"

  1. Put your content image under "Test/content" and your style image under "Test/style"

  2. For single style transfer, run

$ python eval.py

The stylized images will be in folder "Test/result" 4. For multiple style transfer, run

$ python eval_multiple_style.py
  1. For real-time demo, run
$ python real-time-demo.py --style_image Test/style/picasso_self_portrait.jpg
  1. For training, put the training images under the folder "train_data"

download MS-COCO dataset from https://cocodataset.org/#home and put it under "train_data/content" download Wikiart from https://www.wikiart.org/ and put them under "train_data/style" then run,

$ python train.py

Special thanks to the contributions of Jakub M. Tomczak for their LT on their LT computation

You might also like...
Official PyTorch implementation of
Official PyTorch implementation of "ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows"

ArtFlow Official PyTorch implementation of the paper: ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows Jie An*, Siyu Huang*, Yibing

null 123 Dec 27, 2022
This is the official Pytorch implementation of the paper
This is the official Pytorch implementation of the paper "Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative Model"

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

Soomin Park 28 Dec 16, 2022
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model The task of age transformation illustrates the change of an individual

null 444 Dec 30, 2022
Fast Neural Style for Image Style Transform by Pytorch
Fast Neural Style for Image Style Transform by Pytorch

FastNeuralStyle by Pytorch Fast Neural Style for Image Style Transform by Pytorch This is famous Fast Neural Style of Paper Perceptual Losses for Real

Bengxy 81 Sep 3, 2022
PyTorch implementation of neural style transfer algorithm
PyTorch implementation of neural style transfer algorithm

neural-style-pt This is a PyTorch implementation of the paper A Neural Algorithm of Artistic Style by Leon A. Gatys, Alexander S. Ecker, and Matthias

null 770 Jan 2, 2023
Neural style transfer in PyTorch.
Neural style transfer in PyTorch.

style-transfer-pytorch An implementation of neural style transfer (A Neural Algorithm of Artistic Style) in PyTorch, supporting CPUs and Nvidia GPUs.

Katherine Crowson 395 Jan 6, 2023
Neural style transfer as a class in PyTorch

pt-styletransfer Neural style transfer as a class in PyTorch Based on: https://github.com/alexis-jacq/Pytorch-Tutorials Adds: StyleTransferNet as a cl

Tyler Kvochick 31 Jun 27, 2022
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer

Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer Paper on arXiv Public PyTorch implementation of two-stage peer-reg

NNAISENSE 38 Oct 14, 2022
Instant Real-Time Example-Based Style Transfer to Facial Videos
Instant Real-Time Example-Based Style Transfer to Facial Videos

FaceBlit: Instant Real-Time Example-Based Style Transfer to Facial Videos The official implementation of FaceBlit: Instant Real-Time Example-Based Sty

Aneta Texler 131 Dec 19, 2022
Comments
  • No license File

    No license File

    Hi, Great job! Since there is no license file in your project, people can only view your work. Could you please add a license file to your project? Thanks

    opened by ambroiseb 2
  • torch and torchvistion version

    torch and torchvistion version

    Hi,

    I have a question because I'm encountering some problems when trying to run your code:

    Which version of torch and torchvision libs did you use in practice?

    Thansks in advance, nice work!

    opened by tlatkowski 0
Owner
null
GitHub
Code of 3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces

3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces Installation After cloning the repo open

null 37 Dec 3, 2022
CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes Implementation of CoSMA: Convolutional Semi-Regular Mesh Autoencoder arXiv p

Fraunhofer SCAI 10 Oct 11, 2022
Clockwork Variational Autoencoder

Clockwork Variational Autoencoders (CW-VAE) Vaibhav Saxena, Jimmy Ba, Danijar Hafner If you find this code useful, please reference in your paper: @ar

Vaibhav Saxena 35 Nov 6, 2022
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech

VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our rece

Jaehyeon Kim 1.7k Jan 8, 2023
Recurrent Variational Autoencoder that generates sequential data implemented with pytorch

Pytorch Recurrent Variational Autoencoder Model: This is the implementation of Samuel Bowman's Generating Sentences from a Continuous Space with Kim's

Daniil Gavrilov 347 Nov 14, 2022
Variational autoencoder for anime face reconstruction

VAE animeface Variational autoencoder for anime face reconstruction Introduction This repository is an exploratory example to train a variational auto

Minzhe Zhang 2 Dec 11, 2021
PyTorch Autoencoders - Implementing a Variational Autoencoder (VAE) Series in Pytorch.

PyTorch Autoencoders Implementing a Variational Autoencoder (VAE) Series in Pytorch. Inspired by this repository Model List check model paper conferen

Subin An 8 Nov 21, 2022
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder

RAVE: Realtime Audio Variational autoEncoder Official implementation of RAVE: A variational autoencoder for fast and high-quality neural audio synthes

ACIDS 587 Jan 1, 2023
The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

ASMA-GAN Anisotropic Stroke Control for Multiple Artists Style Transfer Proceedings of the 28th ACM International Conference on Multimedia The officia

Six_God 146 Nov 21, 2022
Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization

Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization Official PyTorch implementation for our URST (Ultra-Resolution Sty

czczup 148 Dec 27, 2022