SystemErrorWang/White-box-Cartoonization

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 repository includes a model weight converter that enables the translation of checkpoints across different runtime environments. This utility performs weight key remapping and tensor dimension permutation to ensure compatibility between framework implementations. The system supports AI photo stylization through pre-trained weight loading and provides configurable upsampling alignment to maint

photo2cartoon is a vision-based software tool and training framework designed to convert real human portrait photographs into stylized cartoon images. It utilizes generative adversarial networks to translate images from a real-world domain to a cartoon style. The project includes a training framework for these models that supports paired-data supervision and multi-GPU distributed training. It employs identity-preserving loss functions to ensure that the resulting cartoon outputs retain the original facial features of the subject. The system incorporates a full preprocessing pipeline that han

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 repository is a deep learning educational resource and a neural network project suite. It provides a collection of practical TensorFlow implementations and coding projects designed to demonstrate the application of various neural network architectures to real-world data. The project includes specific samples for generative adversarial networks, focusing on synthetic image generation and style translation. It also provides examples of deep learning model construction across different learning paradigms. The codebase covers a broad range of capabilities, including computer vision for imag