Ai weirdness candy hearts free#

There are several phrases you can add that seem to make things better, like 'trending on artstation' or ' unreal engine' (a fancy new video game rendering engine). A herd of sheep grazing on a lush green hillside | amazing awesome and epicĪnd how good a picture you get depends on exactly how you ask for it. 'Oh,' goes the neural net, 'you wanted a GOOD picture'. If I use the same prompt and add 'Amazing awesome and epic', the picture gets noticeably better. And did it add palm trees? The original image's composition is destroyed and what is left looks pretty flat. The answer is: sorta? A herd of sheep grazing on a lush green hillsideĬan't really tell whether those are sheep or cauliflower. With the image on the right as a starting point, would VQGAN simply add the sheep? In fact, Azure image description had still called it 'a herd of sheep grazing on a lush green hillside' even after I had removed all the sheep. So I decided to start with an image that a different neural net had captioned as 'a herd of sheep grazing on a lush green hillside'. The sheep are not so much grazing as embedded like weird molars, and the hillside isn't very picturesque.īut this version of VQGAN+CLIP allows me to upload an image as a starting point. Here's what it generated after 450 iterations: Prompt: A herd of sheep grazing on a lush green hillside I decided to give the VQGAN + CLIP method of steering a try (tutorial and link here), and for its first task I decided to have it generate a subject that's given neural nets trouble in the past: 'a herd of sheep grazing on a lush green hillside'.

I've used this to generate such useful things as Frodo Baggins delivering pizza through the Mines of Moria, cursed candy hearts, fully illustrated sea shanties, and more fully illustrated sea shanties. Since CLIP technically only judges how well a picture matches a prompt, people have developed a few ways of using CLIP's judgements to aim an image generator.

Ai weirdness candy hearts free#

One is that, instead of ominous grey geometric blobs when it doesn't understand your prompt (there is a free interactive demo of AttnGAN here and it is a lot of fun), a huge neural net like CLIP can follow a wide array of prompts. There are upsides to working with a neural net that trained on a huge collection of internet images and text.