StyleCLIP VS pixel2style2pixel

While CLIP on its own is useful for applications such as zero-shot classification, semantic searches, and unsupervised data exploration, CLIP is also used as a building block in a vast array of multimodal applications, from Stable Diffusion and DALL-E to StyleCLIP and OWL-ViT. For most of these downstream applications, the initial CLIP model is regarded as a “pre-trained” starting point, and the entire model is fine-tuned for its new use case.

I'm currently building a fork for StyleCLIP global directions which allows you to control multiple semantic parameters simoultaneously to generate and edit an image with StyleGAN and CLIP in realtime. I want to showcase its potential as a design tool. Unfortunately, GAN weights are trained on very domain-specific (faces, cars, churches) data. This makes them inferior to modern diffusion models which I can use to generate whatever comes to mind. Although I know we won't have a GAN-based DALL-E counterpart anytime soon, I still would love to use my system with weights that can output a wide variety of things.

(Added Feb. 15, 2021) StyleCLIP - Colaboratory by orpatashnik. Uses StyleGAN to generate images. GitHub. Twitter reference. Reddit post.

I used StyleCLIP. I purchased some Google Colab time to use their GPUs. I'll probably do some more later this week.

Link for Styleclip

So, I watched this Corridor Crew video on generating realistic faces from CG characters, and I wanted to try it out on the RDR2 models. The github link for the original work is here. If you guys are interested I can generate the faces of more characters from RDR2 and RDR1. I can even try some from RD Revolver.

Combining these methods with CLIP allows you to generate images based on text. This one uses a face generator. https://github.com/orpatashnik/StyleCLIP

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.