Put pretrained model into the "model" folder
change line #96-97 of SnDserver.py

MODELS = 'models/'  # path to model directory
names = [os.path.join(MODELS,'G.pth'), os.path.join(MODELS,'E.pth'), os.path.join(MODELS,'EX.pth')]

load sample images into the "public/sample_images/" directory
run with python3 SnDserver.py

MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
[Paper]

Yuheng Li, Krishna Kumar Singh, Utkarsh Ojha, Yong Jae Lee
UC Davis
In arXiv, 2019

1/31/2020 update: Code and models released.

Demo Video

Requirements

Linux
Python 3.7
Pytorch 1.3.1
NVIDIA GPU + CUDA CuDNN

Getting started

Clone the repository

git clone https://github.com/Yuheng-Li/MixNMatch.git
cd MixNMatch

Setting up the data

Download the formatted CUB data from this link and extract it inside the data directory

Downloading pretrained models

Pretrained models for CUB, Dogs and Cars are available at this link. Download and extract them in the models directory.

Evaluating the model

In code

Run python eval.py --z path_to_pose_source_images --b path_to_bg_source_images --p path_to_shape_source_images --c path_to_color_source_images --out path_to_ourput --mode code_or_feature --models path_to_pretrained_models
For example python eval.py --z pose/pose-1.png --b background/background-1.png --p shape/shape-1.png --c color/color.png --mode code --models ../models --out ./code-1.png
- NOTE:(1) in feature mode pose source images will be ignored; (2) Generator, Encoder and Feature_extractor in models folder should be named as G.pth, E.pth and EX.pth

Training your own model

In code/config.py:

Specify the dataset location in DATA_DIR.
- NOTE: If you wish to train this on your own (different) dataset, please make sure it is formatted in a way similar to the CUB dataset that we've provided.
Specify the number of super and fine-grained categories that you wish for FineGAN to discover, in SUPER_CATEGORIES and FINE_GRAINED_CATEGORIES.
For the first stage training run python train_first_stage.py output_name
For the second stage training run python train_second_stage.py output_name path_to_pretrained_G path_to_pretrained_E
- NOTE: output will be in output/output_name
- NOTE: path_to_pretrained_G will be output/output_name/Model/G_0.pth
- NOTE: path_to_pretrained_E will be output/output_name/Model/E_0.pth
For example python train_second_stage.py Second_stage ../output/output_name/Model/G_0.pth ../output/output_name/Model/E_0.pth

Results

1. Extracting all factors from differnet real images to synthesize a new image

2. Comparison between the feature and code mode

3. Manipulating real images by varying a single factor

4. Inferring style from unseen data

Cartoon -> image	Sketch -> image

5. Converting a reference image according to a reference video

Citation

If you find this useful in your research, consider citing our work:

@inproceedings{li-arxiv2019,
  title = {MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation},
  author = {Yuheng Li and Krishna Kumar Singh and Utkarsh Ojha and Yong Jae Lee},
  booktitle = {arXiv},
  year = {2019}
}

AustinXY/mixnmatch_demo

MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation [Paper]