Awesome GitHub RepositoriesAdaptive Instance Normalization

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

StyleGAN2 is a TensorFlow generative adversarial network and image synthesis model designed to produce high-resolution synthetic visual content. It functions as a deep learning architecture that learns patterns from image datasets to synthesize new images. The project includes a latent space projection tool for mapping existing images to latent vectors to analyze their representation within a generative model. It also provides an image quality evaluation framework to measure the visual fidelity and diversity of synthetic outputs. The system covers the full generative pipeline, including imag

FastPhotoStyle 是一款 AI 图像风格化工具和深度学习风格迁移框架。它作为一种基于特征的图像转换器，利用深度神经网络将参考图像的艺术视觉特征应用到目标照片上。 该项目通过使用前馈网络实现实时图像风格化。这使得系统能够以单次传递（single pass）的方式执行转换，而无需使用迭代优化。 该框架涵盖了 AI 照片编辑和深度学习视觉特效，特别专注于图像纹理和颜色的转换，以将摄影内容与艺术风格相融合。

SPADE is a semantic image synthesis framework and generative adversarial network designed to transform semantic label maps into photorealistic images. It uses a spatially-adaptive normalization model to modulate activations based on semantic maps, ensuring that spatial layouts and details are preserved throughout the synthesis process. The project enables the generation of diverse image variations from a single semantic layout by integrating variational autoencoders and latent vector style control. These mechanisms allow for the adjustment of visual appearances and textures while keeping the

UGATIT is an unsupervised generative adversarial network and image-to-image translation model implemented in TensorFlow. It serves as the official research implementation of an ICLR 2020 paper, providing a framework for converting images between different visual styles without requiring paired training examples. The system utilizes an unsupervised generative attentional network and attention maps to deform geometric shapes and modify textures during the translation process. It employs a cycle-consistent framework to ensure translation quality by requiring images to return to their original st

SimSwap 是一个基于 PyTorch 构建的深度学习换脸框架与计算机视觉媒体处理器。它作为一种图像合成工具，旨在利用单个训练模型将图像与视频中的人物身份替换为目标人脸。 该系统作为视频身份替换工具运行，在保持源媒体原始表情与光照的同时，跨帧交换身份。它通过自动化的面部特征映射，实现了数字身份操纵与合成媒体的制作。 该框架既支持应用训练好的模型在媒体中进行换脸，也支持使用特定图像数据集训练自定义换脸模型。