real time face swap and one-click video deepfake with only a single image
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Updated
Oct 28, 2025 - Python
Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator network. GANs have been shown to be powerful generative models and are able to successfully generate new data given a large enough training dataset.
real time face swap and one-click video deepfake with only a single image
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
Image-to-Image Translation in PyTorch
A list of all named GANs!
A MNIST-like fashion product database. Benchmark 👇
Keras implementations of Generative Adversarial Networks.
so-vits-svc fork with realtime support, improved interface and more features.
PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Synthesizing and manipulating 2048x1024 images with conditional GANs
StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models
Gluon CV Toolkit
An arbitrary face-swapping framework on images and videos with one single trained model!
Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
人像卡通化探索项目 (photo-to-cartoon translation project)
Interactive Image Generation via Generative Adversarial Networks
Collection of generative models in Tensorflow
Compute FID scores with PyTorch.
Released June 10, 2014