Clip-App: CLIP + VQGAN Win Interface 1.0

Examples Pictures by fellow supporter Steven First

A GUI to input a small sentence and generate a 2D image from it using AI.

It is based on the colab file from Katherine Crowson: 

https://colab.research.google.com/drive/1go6YwMFe5MX6XM9tv-cnQiSTU50N9EeT

If your computer can't run the app, you can use the colab for very similar results.

There is more updates on the app on my Patreon:

https://www.patreon.com/DAINAPP

The app is very similar to the colab, just with a few changes to the memory usage and an extra option or two. I plan to keep updating it on Patreon with more improvements.

Ok, what is this?

This is a GUI that combine two AI Architectures, CLIP + VQGAN. It let you write a text and it will generate a image based on that text.

There is a reddit for those type of images, you can check it out here:

https://www.reddit.com/r/deepdream/

How to use it:

This application only work in a decent speed using a Nvidia Graphic card, will take a long time to generate images using the CPU. (AMD cards don't work)

You write whatever you want the the text input box, and it will try to generate a image based on that.

Each line will be a new image. There is some tricks the deepdream community found during testing. Like adding Unreal Engine at the text might create more crisp results.

Using the special character | it will let the AI separate the word tokens, but usually I don't like the results using this character.

Usually you want something between 1200 and 1600 loops to generate a decent image.

First time using it might download a ~500mb model file.

Memory Usage:

This is where it get tricky.

There is a option that affect the VRAM that the app need. But even so it's require a lot of memory.

4gb VRAM:

• Can generate 200X200 images using the Resample Image Model
• Cannot generate images using the Konia Image Model

8gb VRAM:

• Can generate 400X400 images using the Resample Image Model
• Can generate 200X200 images using the Konia Image Model
• Konia model use more VRAM, but it also produce better results. I will try to improve Vram usage, but can't guarantee it.

Once the app is more complete I will release the source on Github, just like Dain-App and FlowBlur-App