All Projects → zsdonghao → Text To Image

zsdonghao / Text To Image

Generative Adversarial Text to Image Synthesis / Please Star -->

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Text To Image

Deep Learning Book
《Deep Learning》《深度学习》 by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Stars: ✭ 492 (-1.2%)
Mutual labels:  gan, tensorlayer
Cyclegan tensorlayer
Re-implement CycleGAN in Tensorlayer
Stars: ✭ 86 (-82.73%)
Mutual labels:  gan, tensorlayer
Srgan
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Stars: ✭ 2,641 (+430.32%)
Mutual labels:  gan, tensorlayer
Tensorlayer
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥
Stars: ✭ 6,796 (+1264.66%)
Mutual labels:  gan, tensorlayer
Dcgan
The Simplest DCGAN Implementation
Stars: ✭ 286 (-42.57%)
Mutual labels:  gan, tensorlayer
Deep Learning Resources
由淺入深的深度學習資源 Collection of deep learning materials for everyone
Stars: ✭ 422 (-15.26%)
Mutual labels:  gan
Cool Fashion Papers
👔👗🕶️🎩 Cool resources about Fashion + AI! (papers, datasets, workshops, companies, ...) (constantly updating)
Stars: ✭ 464 (-6.83%)
Mutual labels:  gan
Tensorflow Tutorial
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
Stars: ✭ 4,122 (+727.71%)
Mutual labels:  gan
Simgan Captcha
Solve captcha without manually labeling a training set
Stars: ✭ 405 (-18.67%)
Mutual labels:  gan
Textgan Pytorch
TextGAN is a PyTorch framework for Generative Adversarial Networks (GANs) based text generation models.
Stars: ✭ 479 (-3.82%)
Mutual labels:  gan
Mimicry
[CVPR 2020 Workshop] A PyTorch GAN library that reproduces research results for popular GANs.
Stars: ✭ 458 (-8.03%)
Mutual labels:  gan
Pro gan pytorch
ProGAN package implemented as an extension of PyTorch nn.Module
Stars: ✭ 425 (-14.66%)
Mutual labels:  gan
Tf.gans Comparison
Implementations of (theoretical) generative adversarial networks and comparison without cherry-picking
Stars: ✭ 477 (-4.22%)
Mutual labels:  gan
Wassersteingan.tensorflow
Tensorflow implementation of Wasserstein GAN - arxiv: https://arxiv.org/abs/1701.07875
Stars: ✭ 419 (-15.86%)
Mutual labels:  gan
Deblur Gan
Keras implementation of "DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks"
Stars: ✭ 483 (-3.01%)
Mutual labels:  gan
Simgan
Implementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training
Stars: ✭ 406 (-18.47%)
Mutual labels:  gan
Melgan
MelGAN vocoder (compatible with NVIDIA/tacotron2)
Stars: ✭ 444 (-10.84%)
Mutual labels:  gan
Rgan
Recurrent (conditional) generative adversarial networks for generating real-valued time series data.
Stars: ✭ 480 (-3.61%)
Mutual labels:  gan
Generative Models
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Stars: ✭ 438 (-12.05%)
Mutual labels:  gan
Enlightengan
[IEEE TIP'2021] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
Stars: ✭ 434 (-12.85%)
Mutual labels:  gan

Text To Image Synthesis

This is a tensorflow implementation of synthesizing images. The images are synthesized using the GAN-CLS Algorithm from the paper Generative Adversarial Text-to-Image Synthesis. This implementation is built on top of the excellent DCGAN in Tensorflow.

Plese star https://github.com/tensorlayer/tensorlayer

Model architecture

Image Source : Generative Adversarial Text-to-Image Synthesis Paper

Requirements

Datasets

  • The model is currently trained on the flowers dataset. Download the images from here and save them in 102flowers/102flowers/*.jpg. Also download the captions from this link. Extract the archive, copy the text_c10 folder and paste it in 102flowers/text_c10/class_*.

N.B You can downloads all data files needed manually or simply run the downloads.py and put the correct files to the right directories.

python downloads.py

Codes

  • downloads.py download Oxford-102 flower dataset and caption files(run this first).
  • data_loader.py load data for further processing.
  • train_txt2im.py train a text to image model.
  • utils.py helper functions.
  • model.py models.

References

Results

  • the flower shown has yellow anther red pistil and bright red petals.
  • this flower has petals that are yellow, white and purple and has dark lines
  • the petals on this flower are white with a yellow center
  • this flower has a lot of small round pink petals.
  • this flower is orange in color, and has petals that are ruffled and rounded.
  • the flower has yellow petals and the center of it is brown
  • this flower has petals that are blue and white.
  • these white flowers have petals that start off white in color and end in a white towards the tips.

License

Apache 2.0

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].