Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to look at images, and even videos that were created by an AI,
and honestly, I cannot believe how good some of these are. And,
it is possible that you can run this AI at home yourself? You’ll find out today.
This year, through OpenAI’s DALL-E 2, we are entering the age of AI-driven image generation.
Most of these techniques take a text prompt, which means that we can write
whatever we wish to see on the screen, and first, a noise pattern
appears that slowly morphs into exactly what we are looking for.
This is what we mean when we say we are talking about diffusion-based models.
Now, OpenAI’s DALL-E 2 can create incredibly creative images,
and Google’s Parti and Imagen AIs are also at the very least as good.
Sometimes they even win linguistic battles against OpenAI’s solution.
But there is a problem. All of them are missing something. And that is the model weights,
and the source code. This means that these are all closed solutions,
and we cannot pop the hood and look around inside.
But now, here is a new solution called Stable Diffusion, where the model weights and
the full source code are available. I cannot overstate how amazing this is.
So, to demonstrate why this is so amazing, here are two reasons.
Reason number one is that with this, we can finally take out our digital wrench
and tinker with it. For instance, we can now adjust the internal parameters
in ways that we cannot do with the closed solutions like DALL-E 2 and
Imagen. So now, let’s have a look together at 10 absolutely amazing examples of what it can do!
After that, I’ll tell you about reason number two of how it gets even better.
One, dreaming. Since the internal parameters are exposed, we can add small changes to them,
create a bunch of outputs that are similar, and then finally, stitch these images together
as a video. This is so much better for exploring ideas, just imagine that sometimes you get an
image that is almost what you are looking for, but the framing, or the shape of the doggy is
not exactly perfect, well, you won’t need to throw out these almost perfect solutions anymore. Look
at that. With this, we can make the perfect good boy so much easier. I absolutely love it. Wow.
Two, interpolation. Now hold on to your papers, because we can even create a beautiful visual
novel like this one by entering a bunch of prompts, like the ones you see here,
and we don’t go from just one image to the next one in one jarring jump, but instead,
these images can now be morphed into the next one, creating these amazing transitions. By the way,
the links to all of these materials are available in the video description,
including a link to the full version of the video that you see here.
Three, its fantasy imagery are truly something else. Whether you are looking for landscapes,
I was quite surprised by how competent Stable Diffusion is at creating those, these tree houses
are amazing too, but that’s not when I fell off the chair. I fell off the chair when I saw
these realistic fairy princesses. I did not expect it to be able to create
such amazingly realistic humans. How cool is that!
Four, we can also create a collage. Here, we can take a canvas,
enter several prompts, and select a range for each of them. Now the issue is that there is
space between the images, and there is another problem, even if there is no space between them,
they won’t blend into each other. No matter, Stable Diffusion can also
perform image inpainting, which means that we select a region, delete it,
and it will be filled in with information based on its surroundings. And the results are
spectacular. We don’t get many separate images, we get one coherent image instead.
Five, you know what, let’s look at a few more fantasy examples. Here are some of my favorites.
Six, now these are diffusion-based models, which means that they start out from a bunch of noise,
and slowly adjust the pixels of this image to resemble our input text prompts
a little more. Hence, they are very sensitive to the initial noise patterns that we start out from.
Andrej Karpathy found an amazing way to take advantage of this property by adjusting this
noise, but just a tiny bit, and create many new, similar images. When stitched together,
it results in a hypnotic video like this one. Random noise walks if you will. Loving it!
Seven, it can generate not only images, but with a little additional work, even animations.
You are going to love this one. Look. This was made by creating the same image with the eyes open
and closed, and with a little additional work of blending them together,
it looks like this. Once again, the links to all of these works are available
in the video description if you wish to have a closer look at the process.
Eight, you remember that it can create fantastic portraits,
and it can interpolate between them. Now, putting it all together, it can create portraits,
and interpolate between them, creating these sometimes smooth, sometimes a little jumpy videos.
And don’t forget, nine, variant generation
is still possible. We can still give it an input image,
and since it understands what this image depicts, it can also repaint it in different variations.
And finally, ten. The fact that these amazing images come out of Stable Diffusion does not mean
that we have to use them in their entirety. If there is just one part of an image that we like,
be it the knight on a horse, or the castle, that is more than enough. We can discard
the rest of the image and just use the parts that we love best, and make an awesome montage
out of it. I would say that here, very few humans would be able to tell the trick.
Now, we discussed that we can now pop the hood and tinker with this AI,
that was one of the amazing reasons behind these results, but I promised two reasons
why this is so good. So what is reason number two? Is it possible that? Yes!
This is the moment you have been waiting for. You can now try it yourself. If you are patient,
you can engage in changing the internal parameters here and get some amazing variants.
You might have to wait for a bit, but as of the making of this video, it works. Now what happens
when you Fellow Scholars get over there, who really knows, we have crashed plenty of websites
before with our Scholarly Stampede. And, if you don’t want to wait or run some more advanced
experiments, you can run the model yourself at home on a consumer graphics card. Loving it.
And if you are unable to try it, don’t despair,
AI-based image generation is only getting cheaper and more democratized from here on out. So,
a little open-source competition for OpenAI and Google. What a time to be alive!
And, please, as always, whatever you do, do not forget to apply the First Law of Papers,
which says that research is a process. Do not look at where we are,
look at where we will be two more papers down the line. For instance, here are some
results from DALL-E 1, and just a year later, DALL-E 2 was capable of this. Just a year
later. That is unbelievable. Just imagine what we will be able to do 5 years from now.
If you have some ideas, make sure to leave a comment about that below.
So, finally Stable Diffusion, a free and open source solution for AI-based image generation.
Double thumbs up. This is something for everyone out there and it really
shows the power of collaboration as us, tinkerers around the world work together
to make something amazing. I love it. Thank you so much!
And note that all this took about 600 thousand dollars to train. Now, make no mistake, that is a
lot of dollars, but, this also means that creating an AI like this does not cost tens of millions of
dollars anymore, and the team at Stability AI is already working on a smaller and cheaper model
than this. So, we are now entering not only the age of AI-based image generation, but the
age of free and open AI-based image generation. Oh yes! And for now, let the experiments begin!
Thanks for watching and for your generous support, and I'll see you next time!