#image-to-image translation

Delve into high-resolution image-to-image translation using Conditional GANs with this Pytorch project. Transform semantic label maps into realistic images or generate portraits from face labels efficiently. With features like interactive editing and example scripts, utilize Cityscapes datasets for training and testing. Ideal for NVIDIA GPU users, it supports multi-GPU training and Automatic Mixed Precision for speed enhancement. Achieve lifelike street and face edits with detailed guides on installation, training, and using custom datasets.

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.

GAN Compression efficiently reduces computational loads of popular conditional GANs like pix2pix and CycleGAN by up to 29 times while preserving visual quality. Supporting various architectures, including MUNIT, this method ensures broad applicability in image translation. Demos demonstrate models achieving 8FPS on Jetson Nano GPU. Comprehensive resources are available for optimized application.