There are 1 repository under biggan topic.
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
The author's officially unofficial PyTorch BigGAN implementation.
🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
Pytorch implementation of LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS (BigGAN)
Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai
Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Code for: Transforming and Projecting Images into Class-conditional Generative Networks
[CVPR 2022] Unsupervised Image-to-Image Translation with Generative Prior
Official implementation of FQ-GAN
Generate Amazing Anime Pictures With BigGAN. Just Have Fun !!!
BigGAN; Knowledge Distillation; Black-Box; Fast Training; 16x compression
Data Augmentation optimized for GAN
Create music videos using CLIP with BigGAN, DALL-E and StyleGAN
Pytorch implementation of BigGAN Generator with pretrained weights
Reimplementation of the Paper: Large Scale GAN Training for High Fidelity Natural Image Synthesis
The Wandering Dreamer: An Synthetic Feedback Loop
Multi-Variate Temporal GAN for Large Scale Video Generation
Colabs for text prompt steered image generators
5th place solution for Kaggle Generative Dog Images competition
Using Deep Learning to create fake images of games using PyTorch
Music visualizer using audio and semantic analysis to explore BigGAN latent space.
Exploring Generative Adversarial Networks: Improving training techniques
StyleGAN BigGAN for Motorbike Generation
Uses a Pretrained Network (using Google's Deepmind) to generate images.
Improving diversity of class-conditional generative networks (cGANs) for image classification, using sample reweighting and boosting techniques
Creates unique music videos, using a generative adversarial network.
All the computer vision projects and experiments