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pix2pix (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) - This is a PyTorch implementation of the pix2pix algorithm for image-to-image translation. Given a set of images, the model can learn to generate a new image from a different domain that is similar to the input image.
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GauGAN (https://github.com/NVlabs/SPADE) - This is a PyTorch implementation of the SPADE (SPatially-Adaptive (DE)normalization) algorithm, which can generate images from segmentation maps. You can use it to generate realistic images of objects, landscapes, and other scenes.
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BigGAN (https://github.com/ajbrock/BigGAN-PyTorch) - This is a PyTorch implementation of the BigGAN model for generating high-resolution images. It is trained on a large dataset and can generate a wide range of images, including photographs of animals, objects, and landscapes.
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Neural Style Transfer (https://github.com/akaxiaok/neural_style_tf2) - This is a TensorFlow 2.0 implementation of the neural style transfer algorithm, which allows you to transfer the style of one image to another. You can use it to create a new image that combines the content of one image with the style of another
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