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Framework for Neural Style Transfer (NST) built upon PyTorch
The main features of pystiche/pystiche are: Computer Vision, Style Transfer.
Projects with overlapping indexed features include: jantic/deoldify — DeOldify is a deep learning system and a set of pre-trained computer vision models designed to apply realistic colors… progamergov/neural-style-pt — PyTorch implementation of neural style transfer algorithm. alexis-jacq/pytorch-tutorials. darkstar112358/fast-neural-style — pytorch implementation of fast-neural-style. kendricktan/drawlikebobross. stephanzheng/neural-fingerprinting.
pytorch implementation of fast-neural-style
DeOldify is a deep learning system and a set of pre-trained computer vision models designed to apply realistic colors to grayscale photographs and video footage. It functions as a neural media restoration tool that uses trained networks to estimate original hues for black-and-white media and remove glitches and artifacts from aged images and film. The project employs a NoGAN colorization technique that removes the GAN discriminator during training to prevent artifacts and avoid over-saturation of pixels. For cinematic sequences, it applies temporal frame consistency to maintain color stabilit