stylegan2 VS DragGAN

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.

Whatever it struggles with today is the thing they improve upon next. They are now literally at the point where you can literally drag faces around and make it look exactly how you want with no expertise. https://vcai.mpi-inf.mpg.de/projects/DragGAN/

code repository here

DragGan had it's source released a couple of days ago. So that should be somewhat usable now. Haven't tried myself.

The source code for the algorithm DragGAN (Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold) released and demo available on Huggingface. [GitHub Link | Huggingface].

Try this out https://github.com/XingangPan/DragGAN

To them, Here you go: Have Fun

GitHub repository