Awesome
Semantic Image Synthesis via Adversarial Learning
This is a PyTorch implementation of the paper MC-GAN: Multi-conditional Generative Adversarial Network for Image (BMVC 2018).
Requirements
- python 2.7
- PyTorch 0.3
- Torchvision
- easydict
- pandas
- torchtext
- NLTK
Model1
please download a pre-trained model bird and base image for training base_img_bird, base_img_flower
Additional Datasets
Prepare the Caltech-200 birds dataset and Oxford-102 flowers dataset for reproducing main results in this repository
Run Model1
Train a MC-GAN model on the bird (CUB) dataset using our preprocessed embeddings: python main.py --cfg cfg/birds_3stages.yml --gpu 0
Test a MC-GAN model
Change cfg/*.yml files to generate images from pre-trained models.
- Train.flag = False
- Train.net_G = 'path of pre-trianed model'
result
Model2
please download a pre-trained model bird and text embedding model
Run Model2
- Text embdding
- run train_text_embeddng.py
- Train Generative model
- run train_MCb.py
- Test
- run test.py
please follow result in this repository
Results
##Citing MC-GAN
If you find MC-GAN useful in your research, please consider citing:
@inproceedings{park2018mc,
title={MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis},
author={Park, Hyojin and Yoo, Youngjoon and Kwak, Nojun},
booktitle={The British MachineVision Conference (BMVC)},
year={2018}
}