pix2pix VS pix2pixHD

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

Well, maybe not, considering things like pix2pix can generate detail from just solid shapes and colors.

Sources: U2Net, ArtLine, Pix2PixHD, APDrawingGAN

Pix2PixHD (from DeepSIM)

Uses a mix of instance segmentation (BiSeNet) and conditional GAN, and is heavily inspired by the Pix2PixHD and DeepSIM papers. Will have more details when I wake up!

I used Pix2PixHD implementation in GitHub if you want to see the full code.