The first is cool, the second is great, and the third one is simply incredible to the
point that I couldn’t believe the results and had to look over and over again.
And the fourth is a thing of beauty.
So, what are the tricks?
First, this work is called GigaGAN, and it can perform text to image.
We Fellow Scholars have seen this before many-many times, you enter a text prompt and it paints
you an image.
What is great about it is that it can give us reasonably high quality images, that is
okay, but here is the kicker: it can perform this in a fraction of a second.
That is extremely quick.
For instance, a previous, StyleGAN-based method that could be roughly as fast, we needed to
make significant concessions in terms of quality.
Not anymore.
Loving it.
Two, it is not only fast, but it it so fast that it can create several images per second,
and thus it offers a controllable latent space.
This is a hallmark for GAN-based methods and leads to incredible artistic controllability
as you see here.
But, what does all this that mean?
A GAN-based method means a generative adversarial network where to neural networks battle each
other.
One tries to generate new images to fool the other one, while this other one trains to
be able to spot these synthetic images.
Over time, they battle each other, and they improve together.
Now, third, and this is where things get out of hand.
Super resolution, or in other words, image upscaling.
Here, a coarse image goes in, and the AI guesses what this image could be, and synthesizes
a new, really detailed image.
Now hold on to your papers, and look at that!
The difference can be really huge: up to a 1000 times more pixels in the new image.
So how does it compare to previous methods, for instance, the amazing Stable Diffusion?
Oh my, look at that.
It is so much better across the board, pretty much everywhere.
But the eyes, the eyes are truly something else with the new technique.
What a time to be alive!
And finally, fourth.
It also offers a disentangled latent space for controllability.
Now, I hear you asking, Doctor, what does that mean?
This means that we can control the style in place with text prompts.
So whenever we get a teddy bear that we really like, but we would really like to see it crocheted,
a bit fluffier, or made of denim, we can do that, but, without generating a new teddy
bear.
All the changes are applied to the same subject.
That is incredible.
I also loved the ball example here, this almost looks like an image straight out of a material
modeling paper in computer graphics.
What I absolutely love about this paper is that normally, for these four problems, we
would need four separate tools.
Not anymore!
Today we are getting all of these amazing capabilities with just one tool that can do
all of them, and not only that, but it is so much faster than previous methods while
it is still competitive in terms of visual quality.
It is not the best and the fastest at the same time, not even close, but this tradeoff
is I think an amazing value proposition.
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Thanks for watching and for your generous support, and I'll see
you next time!