Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Overview

StackGAN-v2

Pytorch implementation for reproducing StackGAN_v2 results in the paper StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks by Han Zhang*, Tao Xu*, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas.

Dependencies

python 2.7

Pytorch

In addition, please add the project folder to PYTHONPATH and pip install the following packages:

  • tensorboard
  • python-dateutil
  • easydict
  • pandas
  • torchfile

Data

  1. Download our preprocessed char-CNN-RNN text embeddings for birds and save them to data/
  • [Optional] Follow the instructions reedscot/icml2016 to download the pretrained char-CNN-RNN text encoders and extract text embeddings.
  1. Download the birds image data. Extract them to data/birds/
  2. Download ImageNet dataset and extract the images to data/imagenet/
  3. Download LSUN dataset and save the images to data/lsun

Training

  • Train a StackGAN-v2 model on the bird (CUB) dataset using our preprocessed embeddings:
    • python main.py --cfg cfg/birds_3stages.yml --gpu 0
  • Train a StackGAN-v2 model on the ImageNet dog subset:
    • python main.py --cfg cfg/dog_3stages_color.yml --gpu 0
  • Train a StackGAN-v2 model on the ImageNet cat subset:
    • python main.py --cfg cfg/cat_3stages_color.yml --gpu 0
  • Train a StackGAN-v2 model on the lsun bedroom subset:
    • python main.py --cfg cfg/bedroom_3stages_color.yml --gpu 0
  • Train a StackGAN-v2 model on the lsun church subset:
    • python main.py --cfg cfg/church_3stages_color.yml --gpu 0
  • *.yml files are example configuration files for training/evaluation our models.
  • If you want to try your own datasets, here are some good tips about how to train GAN. Also, we encourage to try different hyper-parameters and architectures, especially for more complex datasets.

Pretrained Model

Evaluating

  • Run python main.py --cfg cfg/eval_birds.yml --gpu 1 to generate samples from captions in birds validation set.
  • Change the eval_*.yml files to generate images from other pre-trained models.

Examples generated by StackGAN-v2

Tsne visualization of randomly generated birds, dogs, cats, churchs and bedrooms

Citing StackGAN++

If you find StackGAN useful in your research, please consider citing:

@article{Han17stackgan2,
  author    = {Han Zhang and Tao Xu and Hongsheng Li and Shaoting Zhang and Xiaogang Wang and Xiaolei Huang and Dimitris Metaxas},
  title     = {StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks},
  journal   = {arXiv: 1710.10916},
  year      = {2017},
}
@inproceedings{han2017stackgan,
Author = {Han Zhang and Tao Xu and Hongsheng Li and Shaoting Zhang and Xiaogang Wang and Xiaolei Huang and Dimitris Metaxas},
Title = {StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks},
Year = {2017},
booktitle = {{ICCV}},
}

Our follow-up work

References

  • Generative Adversarial Text-to-Image Synthesis Paper Code
  • Learning Deep Representations of Fine-grained Visual Descriptions Paper Code
Owner
Han Zhang
Han Zhang
EXplainable Artificial Intelligence (XAI)

EXplainable Artificial Intelligence (XAI) This repository includes the codes for different projects on eXplainable Artificial Intelligence (XAI) by th

4 Nov 28, 2022
code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Overview This code is for paper: Not All Unlabeled Data are Equa

Jason Ren 22 Nov 23, 2022
MEND: Model Editing Networks using Gradient Decomposition

MEND: Model Editing Networks using Gradient Decomposition Setup Environment This codebase uses Python 3.7.9. Other versions may work as well. Create a

Eric Mitchell 141 Dec 02, 2022
BLEURT is a metric for Natural Language Generation based on transfer learning.

BLEURT: a Transfer Learning-Based Metric for Natural Language Generation BLEURT is an evaluation metric for Natural Language Generation. It takes a pa

Google Research 492 Jan 05, 2023
The 2nd Version Of Slothybot

SlothyBot Go to this website: "https://bitly.com/SlothyBot" The 2nd Version Of Slothybot. The Bot Has Many Features, Such As: Moderation Commands; Kic

Slothy 0 Jun 01, 2022
Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets).

TOQ-Nets-PyTorch-Release Pytorch implementation for the Temporal and Object Quantification Networks (TOQ-Nets). Temporal and Object Quantification Net

Zhezheng Luo 9 Jun 30, 2022
Fast and Context-Aware Framework for Space-Time Video Super-Resolution (VCIP 2021)

Fast and Context-Aware Framework for Space-Time Video Super-Resolution Preparation Dependencies PyTorch 1.2.0 CUDA 10.0 DCNv2 cd model/DCNv2 bash make

Xueheng Zhang 1 Mar 29, 2022
Apply a perspective transformation to a raster image inside Inkscape (no need to use an external software such as GIMP or Krita).

Raster Perspective Apply a perspective transformation to bitmap image using the selected path as envelope, without the need to use an external softwar

s.ouchene 19 Dec 22, 2022
RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into tables through jointly extracting intervention, outcome and outcome measure entities and their relations.

Randomised controlled trial abstract result tabulator RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into

2 Sep 16, 2022
Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation"

Implicit-Semantic-Response-Alignment Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation" Prerequisites pyt

4 Dec 19, 2022
Implementation of Nyström Self-attention, from the paper Nyströmformer

Nyström Attention Implementation of Nyström Self-attention, from the paper Nyströmformer. Yannic Kilcher video Install $ pip install nystrom-attention

Phil Wang 95 Jan 02, 2023
Decision Transformer: A brand new Offline RL Pattern

DecisionTransformer_StepbyStep Intro Decision Transformer: A brand new Offline RL Pattern. 这是关于NeurIPS 2021 热门论文Decision Transformer的复现。 👍 原文地址: Deci

Irving 14 Nov 22, 2022
ElasticFace: Elastic Margin Loss for Deep Face Recognition

This is the official repository of the paper: ElasticFace: Elastic Margin Loss for Deep Face Recognition Paper on arxiv: arxiv Model Log file Pretrain

Fadi Boutros 113 Dec 14, 2022
Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow.

Denoised-Smoothing-TF Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow. Denoised Smoothing is

Sayak Paul 19 Dec 11, 2022
Tensorflow implementation of Swin Transformer model.

Swin Transformer (Tensorflow) Tensorflow reimplementation of Swin Transformer model. Based on Official Pytorch implementation. Requirements tensorflow

167 Jan 08, 2023
End-To-End Crowdsourcing

End-To-End Crowdsourcing Comparison of traditional crowdsourcing approaches to a state-of-the-art end-to-end crowdsourcing approach LTNet on sentiment

Andreas Koch 1 Mar 06, 2022
Collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and related datasets

The repository collects many various multi-modal transformer architectures, including image transformer, video transformer, image-language transformer, video-language transformer and related datasets

Jun Chen 139 Dec 21, 2022
PyArmadillo: an alternative approach to linear algebra in Python

PyArmadillo is a linear algebra library for the Python language, with an emphasis on ease of use.

Terry Zhuo 58 Oct 11, 2022
GANfolk: Using AI to create portraits of fictional people to sell as NFTs

GANfolk are AI-generated renderings of fictional people. Each image in the collection was created by a pair of Generative Adversarial Networks (GANs) with names and backstories also created with AI.

Robert A. Gonsalves 32 Dec 02, 2022