pix2pix VS dataset-tools

This project uses the pix2pix Image translation architecture (https://phillipi.github.io/pix2pix/) for 3D image post-processing. The goal was to test the 3D applications of this type of architecture.

It seems like this even would make for a cool website, like pix2pix

Most improvements from recent GANs' papers usually tinker with the network architecture or loss functions used. Eg. recent near-human quality StyleGANv2 used progressive growing networks, or the GAN that coined image-to-image translation used modified loss function to better learn mapping between images. You can try explore those two factors

I remember using the original source https://github.com/phillipi/pix2pix and I really did not modify much. For the face pictures I used 600 train pics and 5 hours (Azure NV6).

pix2pix, vid2vid a bit harder to use, especially if you don't know your way around a linux shell. definitely possible to get some cool results though.

I made it automatically with a dataset tool. Here is the link: https://github.com/dvschultz/dataset-tools

I am very glad that my work aroused interest. Thank you all! I will try to answer the questions. How did i create this? I collected a dataset of about 600 images of dark art sketches, processed them so that they would be suitable for training in the StyleGAN2-ada (resized to 1024x1024, edited something a little, made sure that all images have 3 channels). Mainly used photoshop, also Duplicate Photos Fixer Pro to find duplicates, and also I highly recommend Derek's dataset-tools for preparing datasets. Then the dataset was archived, uploaded to GoogleDrive and added to Google Colab for training. I had to subscribe to the Colab Pro because the free version could not start training due to lack of memory. A pro subscription costs $10 per month and provides advantages in capacity and uptime. More about working with Google Colab can be found here. I'm a beginner myself, so no secret techniques have been applied. In fact, everything is as in the video tutorial. Training took place about 30 hours, partly at Tesla P100 and partly on a more powerful Tesla V100. I have not written anywhere an article about this project because I do not speak English very well. And there is not much to write about, everything is simple. In the future I will probably post the .pkl file to the public.