pytorch-CycleGAN-and-pix2pix

This repository provides PyTorch implementations of two image-to-image translation models: CycleGAN and pix2pix. Image-to-image translation means automatically transforming an image from one visual style or domain into another — for example, turning a photograph of a horse into one that looks like a zebra, converting a sketch into a realistic photo, or changing a daytime scene into a nighttime one.

Pix2pix is a paired method: it learns from a dataset of matching before-and-after image pairs, learning the transformation from one to the other. CycleGAN is an unpaired method: it can learn to translate between two categories of images without needing paired examples. It does this using a cycle-consistency idea — if you translate a horse photo to look like a zebra, then translate that back, you should get the original horse back. This constraint teaches the model the transformation without paired training data.

Both models are built on GANs (Generative Adversarial Networks), an AI technique where two neural networks compete: one tries to generate realistic images, the other tries to detect fakes. The code runs in Python using PyTorch and supports multi-GPU training. You would use this repository for research into image generation, creative style transfer, domain adaptation experiments, or as a baseline for building new image translation methods.