pix2pix VS dataset-tools

I have previously worked on retraining Pix2Pix GAN for image-to-image style transfer retrained with image-mask pairs. I expect Stable Diffusion to be better than Pix2Pix, but the problem sounds like something that should have been tackled already. I am familiar with text-based instructions for style transfer using SD (Instruct Pix2Pix), but retraining with image-mask pairs should provide better results. Does anyone know if anything that like exists already? Reference for Pix2Pix https://phillipi.github.io/pix2pix/

Additionally, there are some open source AI models for texture generation, such as pix2pix: https://phillipi.github.io/pix2pix/.

Sounds fun. Also sounds like image-to-image translation in 1D? Pix2pix is a famous implementation that uses UNet + adversarial loss: https://github.com/phillipi/pix2pix

I guess everything is rasterized correctly. While u/Kelaifu is correct: there are no softwares that can guess your face where it's missing, right now there are machine learning techniques and implementations (see: https://phillipi.github.io/pix2pix/) that can go pretty far with guesses.

[Image generated using Machine Learning algorithm pix2pix trained by me using this dataset: CMP Facade Dataset]

pix2pix can already transform images between satellite and maps (https://phillipi.github.io/pix2pix/). Will there be any difference if I use pix2pix to transform satellite images to maps and back?

# Resize all images in a folder by 1/2 # modified from https://github.com/phillipi/pix2pix/blob/master/scripts/combine_A_and_B.py # resize_A_to_C.py from pdb import set_trace as st import os import numpy as np import cv2 import argparse parser = argparse.ArgumentParser("resize images by 1/2") parser.add_argument( "--fold_A", dest="fold_A", help="input directory for original images", type=str, default="./test_A", ) parser.add_argument( "--fold_C", dest="fold_C", help="output directory", type=str, default="./test_C" ) args = parser.parse_args() for arg in vars(args): print("[%s] = " % arg, getattr(args, arg)) img_list = os.listdir(args.fold_A) if not os.path.isdir(args.fold_C): os.makedirs(args.fold_C) for name_A in img_list: path_A = os.path.join(args.fold_A, name_A) if os.path.isfile(path_A) and not name_A.startswith( "." ): # skip . and .. and folders im_A = cv2.imread(path_A, cv2.IMREAD_COLOR) # Example: scale_down by factor 1/2 both dimensions scale_down = 0.5 im_C = cv2.resize( im_A, None, fx=scale_down, fy=scale_down, interpolation=cv2.INTER_LINEAR ) # store resized image with same name as in folder A path_C = os.path.join(args.fold_C, name_A) cv2.imwrite(path_C, im_C)

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

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.