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DragGAN is an interactive generative image editor and manipulator that allows users to reshape visual output by moving handle points on a generative network manifold. It functions as a tool for point-based image editing, mapping user-defined coordinate shifts to a generative model's latent space to deform images. The system includes a generative image inversion tool that converts real photographs into latent representations. This process enables the interactive manipulation of non-generated content by bringing real-world images into a compatible format for the generative adversarial network.
StyleGAN is a TensorFlow-based generative adversarial network framework designed for the synthesis of high-resolution synthetic imagery. It utilizes a style-based generator architecture to create realistic visual assets from latent vectors, focusing on the production of high-fidelity images. The system incorporates style mixing and stochastic noise injection to control visual attributes and fine-grained details. It uses adaptive instance normalization and progressive resolution upsampling to manage image quality and variety across different resolutions. The framework covers the full lifecycl
PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative adversarial network architectures. It serves as a toolkit for training and evaluating models that utilize adversarial minimax optimization to produce synthetic data, offering a structured environment for exploring complex generative tasks within the PyTorch ecosystem. The library distinguishes itself through a comprehensive suite of image synthesis and manipulation capabilities, including super-resolution, inpainting, and cross-domain style translation. It supports advanced training m
iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based interface for interactively creating and editing imagery across categories such as landscapes, architecture, and fashion using pre-trained models. The system enables precise control over visual output through latent space exploration, interpolation, and projection. Users can guide the generative process using an interactive editor featuring sketching, coloring, and warping brushes to refine specific regions or shapes in real-time. The project supports both automated scripted gene
DragGAN is an interactive generative AI editor and GAN image editing tool designed for modifying the shape and structure of objects within images. It functions as a latent space manipulator that enables precise geometric and appearance editing by transforming images into editable latent codes.
The main features of xingangpan/draggan are: Point-Based Manipulation, Latent Space Generative Models, Point Tracking, GAN Inversion, Generative Adversarial Image Synthesis, Real Image Inversion, Latent Inversion, Generative AI.
Projects with overlapping indexed features include: opengvlab/draggan — DragGAN is an interactive generative image editor and manipulator that allows users to reshape visual output by moving… nvlabs/stylegan — StyleGAN is a TensorFlow-based generative adversarial network framework designed for the synthesis of high-resolution… eriklindernoren/pytorch-gan — PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative… junyanz/igan — iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based… nvlabs/stylegan2-ada-pytorch — This project is a PyTorch implementation of a generative adversarial network designed for high-resolution image… paddlepaddle/paddlegan — PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework.…