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The author's officially unofficial PyTorch BigGAN implementation.
The main features of ajbrock/biggan-pytorch are: Foundational Generative Models, Generative Models, GANs, VAEs, and AEs.
Open-source alternatives to ajbrock/biggan-pytorch include: nvidia/pix2pixhd — pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into… junyanz/pytorch-cyclegan-and-pix2pix — This project is a deep learning framework designed for training and deploying image-to-image translation models. It… martinarjovsky/wassersteingan — Wasserstein GAN. dmitryulyanov/age — Code for the paper "Adversarial Generator-Encoder Networks". mailmahee/pytorch-generative-adversarial-networks — simple generative adversarial network (GAN) using PyTorch. caogang/wgan-gp — A pytorch implementation of Paper "Improved Training of Wasserstein GANs".
pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework pro
This project is a deep learning framework designed for training and deploying image-to-image translation models. It serves as a research platform for experimenting with neural network architectures that transform visual content between distinct stylistic domains, supporting both paired and unpaired training data. The framework distinguishes itself through its support for cycle-consistency constraints, which allow for image translation between domains without requiring corresponding paired examples. It provides a structured pipeline that utilizes adversarial loss optimization, where generator
A pytorch implementation of Paper "Improved Training of Wasserstein GANs"
