JoJoGAN VS pixel2style2pixel

I've tried style transfer and some GANs like this one: https://github.com/mchong6/JoJoGAN

🔗 https://github.com/mchong6/JoJoGAN

And I quite liked and wanted to train this model with my own dataset, and always fell into the same error CUDA Out of Memory again and again. After searching across the internet for answers ended up finding it -> here.

Code for https://arxiv.org/abs/2112.11641 found: https://github.com/mchong6/JoJoGAN

Since you want to use multiple images for style, it reminded me of this pipeline. In it you can take a dataset of style images and finetune a pretrained model for 500-1000 iterations to achieve the style from these images on new ones. I am not sure if that pipeline is what you need or not though, because it is built for faces in particular, but maybe you can take inspiration from their approach for a generic method.

Here are some models that do that with face images that I have tried out: AnimeGANv3 - this one just came out ArcaneGAN - for faces JoJoGAN - for faces

github: https://github.com/mchong6/JoJoGAN

I wouldn't describe it like that. Consider a simpler example. StyleGAN can make plausible looking face that doesn't look like any of the individual faces it was trained on. It's not making a face collage out of this guy's chin pixels and that guy's eyebrows pixels. There's an easy way to test this: give it a photo of yourself or someone you know with something like pixel2style2pixel and it will probably give you back something convincing. But you weren't in the training. What it's actually doing is interpolating between plausible facial features in a space that it's laid for what human being could conceivably look like.

i think its this one https://github.com/eladrich/pixel2style2pixel

What did you use to generate this? Was it https://github.com/eladrich/pixel2style2pixel or something else? Curious

Right now we have encoders like pSp and restyle or encoder4editing, but how can we tell which one performs better than the other?

The portaitify tool uses pixel2style2pixel to invert a picture into a 'style vector' then generate the corresponding image with the stylegan 2 generator. Happy to a higher or lower level description if that's of interest!

Hi, recently I started playing with ML in python (anaconda in Windows 10), using relevant packages for tensorflow, torch and cuda and running some models. I would like to play with shared projects like the ones in https://paperswithcode.com/, like this one: https://github.com/eladrich/pixel2style2pixel, but many requiere Linux.

I did something like this. Many image GAN papers have implementations on GitHub, just pick the model you want. State-of-the-art image translation is probably something like Pixel2Style2Pixel (https://github.com/eladrich/pixel2style2pixel). Note that there are also wave GANs and they have slightly(?) better audio on average. With image models, typically people input mel spectrograms, which discard the phase information (you could also input 2 channel images for the real and complex parts, but I haven't seen any projects that do that). `librosa` has functions for the Fourier transform and its inverse (Griffin Lim algorithm), but if you want high quality reconstructions try using a neural network solution like WaveGlow to do the inverse conversion (if you're training a GAN, you can fine-tune WaveGlow). The biggest bottleneck is data - get as much data as possible. Also check out /r/machinelearning.