stylegan2
Image generator
Implementation of a generative adversarial network for generating images with conditional variations.
StyleGAN2 - Official TensorFlow Implementation with practical improvements
315 stars
10 watching
67 forks
Language: Jupyter Notebook
last commit: almost 5 years ago Related projects:
| Repository | Description | Stars |
|---|---|---|
| | PyTorch implementation of a scene-based text-to-image generation system with human priors using Generative Adversarial Networks and semantic segmentation | 334 |
| | An implementation of a Generative Adversarial Network (GAN) designed to generate diverse types of images from single input images | 286 |
| | This implementation enables text-to-image generation by leveraging cross-modal contrastive learning. | 98 |
| | A Generative Adversarial Networks implementation for modeling illustrations using a custom dataset of anime faces. | 269 |
| | A Python implementation of a Generative Adversarial Network (GAN) model used to generate portraits from latent codes. | 40 |
| | Generates images using a Deep Convolutional Generative Adversarial Network (DCGAN) algorithm | 936 |
| | A PyTorch implementation of an image-to-image translation model that generates new images from paired training data. | 1,491 |
| | A service for generating new images by mixing the content of an input image with the style of another image. | 51 |
| | A deep learning implementation of a generative adversarial network for generating images from text descriptions. | 340 |
| | This repository provides code for training Generative Adversarial Networks (GANs) for various image datasets, including face generation. | 285 |
| | A TensorFlow implementation of generating images from text descriptions using a Generative Adversarial Network (GAN) architecture | 602 |
| | A PyTorch implementation of Generative Adversarial Networks for anime face drawing | 1,282 |
| | A software framework for generating art-inspired images using deep learning techniques | 412 |
| | Implementation of a deep learning model for generating high-quality images with improved stability and variation. | 538 |
| | A project that utilizes Generative Adversarial Networks to colorize grayscale images | 19 |