stylegan-xl VS StyleCLIP

You should be able to use the (pivotal tuning) inversion script from here: https://github.com/autonomousvision/stylegan_xl/blob/main/run_inversion.py

Model.

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