matchbox-twelvy VS pytorch-CycleGAN-and-pix2pix

Always my goto for any batch image manipulation. The documented examples are varied and helpful, but it takes a little while to get used to.

Here's one use of it in the wild, which batch takes a path of GAN output files, each with a grid of thumbnails, and splits them into individual images. Gloriously easy. https://github.com/binarymax/matchbox-twelvy/blob/master/dcg...

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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.

Using junyanz/pytorch-CycleGAN-and-pix2pix as a basis for pix2pix, I applied the same blending method to fix seams. It essentially takes an input image and generates an output. The results depend on the paired training data. In this case, each map (height, roughness, etc.) is a separate checkpoint and had to be trained on paired training data with the diffuse as the input and the respective map as the output.

Code for https://arxiv.org/abs/1611.07004 found: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

pix2pix algorithm is used for translating Civ6Maps to heightmaps. Synthesized terrain was rendered in blender.

https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

I'm working on creating a face-to-wojak model using PyTorch CycleGan/Pix2Pix [0] and found some of my outputs to be outrageous yet somehow relatable. People are into it so thought I'd share on HN

[0] https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix