real time face swap and one-click video deepfake with only a single image
- ai-face
- deepfake
- video-deepfake
- deepfake-webcam
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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, ... 🧠
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.
Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
Official Code for DragGAN (SIGGRAPH 2023)
SOTA Open Source TTS
Image-to-Image Translation in PyTorch
An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System
[NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer
PyTorch implementations of Generative Adversarial Networks.
Machine learning, in numpy
StyleGAN - Official TensorFlow Implementation
A MNIST-like fashion product database. Benchmark :point_down:
StyleGAN2 - Official TensorFlow Implementation
An elegant PyTorch deep reinforcement learning library.
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
Interactive Image Generation via Generative Adversarial Networks
:stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, French, Korean, Chinese, German and Easy to adapt for other languages)
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
Generative Adversarial Text to Image Synthesis / Please Star -->
We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.