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This project is a PyTorch implementation of AnimeGANv2, a generative adversarial network and image-to-image translation model designed to transform real-world photographs into stylized anime imagery.
The main features of bryandlee/animegan2-pytorch are: Image Stylization GANs, Image-to-Image Translation, Generative Adversarial Networks, PyTorch Implementations, Anime Style Transfer, Anime Style Image Generation, Pre-trained Weight Loading, Model Weight Converters.
Projects with overlapping indexed features include: systemerrorwang/white-box-cartoonization — White-box Cartoonization is a machine learning model and research implementation that transforms real photographs into… tachibanayoshino/animeganv2 — AnimeGANv2 is a generative adversarial network training framework and image stylization tool designed to convert… affinelayer/pix2pix-tensorflow — This project is a TensorFlow implementation of an image-to-image translation framework based on conditional generative… lucidrains/stylegan2-pytorch — This project is a PyTorch implementation of StyleGAN2, providing a library and research framework for training… paddlepaddle/paddlegan — PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework.… dragen1860/tensorflow-2.x-tutorials — This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a…
White-box Cartoonization is a machine learning model and research implementation that transforms real photographs into cartoon-style images. It utilizes a generative adversarial network to apply artistic styles to photographic input. The project employs a white-box representation decomposition method to separate images into surface, structure, and texture components. This approach allows for the targeted stylization of each element during the transformation process. The system includes a multi-stage training pipeline for processing custom datasets and supports inference using pre-trained wei
AnimeGANv2 is a generative adversarial network training framework and image stylization tool designed to convert real-world photographs and videos into anime-style imagery. It functions as an anime style generator that transforms real-world scenes into animation through supervised style transfer. The project provides a system for training style models and extracting specific generator weight parameters from deep learning checkpoints to create lightweight models for inference. It focuses on landscape image stylization and the ability to mimic specific artistic styles from provided datasets. T
This project is a TensorFlow implementation of an image-to-image translation framework based on conditional generative adversarial networks. It provides the tools to train models that map input images to output images based on learned visual patterns, as well as a server for processing image translation requests and serving trained model checkpoints to web clients. The framework includes a system for converting trained model weights into a portable format for browser-based inference. It also features a validation process that generates comparative reports by analyzing input, output, and targe
PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework. It serves as a toolkit for image and video synthesis, providing a collection of generative adversarial network implementations for creating synthetic visual content. The library focuses on advanced synthesis capabilities, including the generation of talking heads through lip motion synchronization and the creation of synthetic videos via motion transfer from driving sequences. It provides tools for domain-to-domain translation, allowing for image style transfer and the transfo