pix2pix VS stylegan2

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 don't know. If you're really curious, you can just try it: https://github.com/NVlabs/stylegan2

StyleGAN2 (Dec 2019) - Karras et al. and Nvidia

> The denoising part of a denoising autoencoder refers to the noise applied to its input

Agree, it converts a noisy image to a denoised image. But the odd thing is, when you put a noisy image into a StyleGAN2 encoder, you get latents which the decoder will turn into a de-noised image. So in practical use, you can take a trained StyleGAN2 encoder/decoder pair and use it as if it was a denoiser.

> These differences lead to learned distributions in the latent space that are entirely different

I also agree there. The training for a denoising auto-encoder and for a GAN network is different, leading to different distributions which are sampled for generating the images. But the architecture is still very similar, meaning the limits of what can be learned should be the same.

> Beyond that the comparison just doesn't work, yes there are two networks but the discriminator doesn't play the role of the AE's encoder at all

Yes, the discriminator in a GAN won't work like an encoder. But if you look at how StyleGAN 1/2 are used in practice, people combine it with a so-called "projection", which is effectively an encoder to convert images to latents. So people use a pipeline of "image to latent encoder" + "latent to image decoder".

That whole pipeline is very similar to an auto-encoder. For example, here's an NVIDIA paper about how they round-trip from image to latent to image with StyleGAN: https://arxiv.org/abs/1912.04958 My interpretation of what they did in that paper is that they effectively trained a StyleGAN-like model with the image L2 loss typically used for training a denoising auto-encoder.

These problems are usually done with CNN Encoder-Decoder frameworks. Usually GAN (Generative Adversarial Networks see StyleGan2).

This is the result of two different models. The first looks like a latent space interpolation of StyleGan2 and the mouth movements are without a doubt from wav2lip.

OP: if you want to run this at higher resolution, you should probably look at running it yourself, using something like this: https://github.com/NVlabs/stylegan2

StyleGAN2 (Dec 2019) - Karras et al. and Nvidia

A few of you might've seen an earlier post of mine about this project (Or the repost that got more upvotes 🙃), and I've improved the code and network since then after more thoroughly reading and understanding the official StyleGAN2 implementation.

So I've been a Pro+ subscriber since around November which I mainly use to train GANs. I have multiple Google accounts, let's call them Account 1, 2, and 3. Accounts 1 and 2 are normal Google accounts and Account 3 is an account I got from my university after I graduated which has unlimited storage.