#CycleGAN

Discover CycleGAN's approach to unpaired image-to-image translation through cycle-consistent GANs. This technique transforms images independently of matching input-output pairs, enabling new artistic style possibilities such as converting Monet paintings to modern photography or altering seasons and objects. Compatible implementations in PyTorch and Tensorflow, with several pre-trained models, are included for immediate use and testing. Find comprehensive setup and training guides to apply this innovative image translation method in both creative and practical contexts.

Discover efficient methods in unpaired image-to-image translation with patchwise contrastive learning. The approach avoids complex loss functions and inverse networks, leading to faster, resource-efficient training compared to CycleGAN. It can be applied to single image training with high-quality results, suitable for a variety of applications. Key benefits include memory efficiency and improved distribution matching, developed collaboratively by UC Berkeley and Adobe Research, and presented at ECCV 2020.

This repository provides PyTorch implementations for both CycleGAN and pix2pix, supporting both unpaired and paired image-to-image translation. Ideal for research and experimentation, the codebase is compatible with the latest PyTorch releases and optimizes training with advanced models like img2img-turbo and contrastive-unpaired-translation. It includes resources such as training tips, FAQs, Docker support, and Colab notebooks for practical usage.

Transform photos into cartoon images while preserving identity with Unpaired Image Translation. Using U-GAT-IT and Soft-AdaLIN, this project balances realism and simplification, refining methods particularly for young Asian female portraits. Explore services or customize with resources available.

Discover how conditional adversarial networks enable efficient image-to-image translation, converting input images into desired outputs with minimal resources. This method supports quick training on small datasets, achieving satisfactory results in creating visual models like architectural facades. Suitable for various applications, it uses multiple datasets, covering scenarios from urban to natural settings. The PyTorch version provides ongoing enhancements, offering performance that matches or exceeds the initial implementation.