This is the code for our ICML 2016 paper on text-to-image synthesis using conditional GANs. You can use it to train and sample from text-to-image models.

This is a pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper, we train a conditional generative adversarial network.

This is a PyTorch-based implementation of the Generative Adversarial Text-to-Image Synthesis paper, utilizing a GAN architecture inspired by DCGAN.

This repository includes PyTorch and Tensorflow implementations of Generative Adversarial Text-to-Image Synthesis used for the final project for Artificial ...

The purpose of this project is to train a generative adversarial network (GAN) to generate images from textual description of the image.

This repository consists of code that is used to convert text-embeddings into Images using Generative Adversarial Networks(GAN)

Generate images based on text descriptions, using Generative Adversarial Networks. Introduction. This is a PyTorch implementation of an end to end Text to Image ...

Pytorch implementation for reproducing AttnGAN results in the paper AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial ...

This is a tensorflow implementation of synthesizing images. The images are synthesized using the GAN-CLS Algorithm from the paper Generative Adversarial Text- ...

Generative adversarial text-to-image synthesis. In Proceedings of The 33rd International Conference on Machine Learning, 2016. [2] Qi Mao, Hsin-Ying Lee ...