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Seven years ago, back in 2015,
one major development in AI research
was automated image captioning.
Machine learning algorithms could
already label objects in images,
and now they learned to put those labels
into natural language descriptions.
And it made one group of researchers curious.
What if you flipped that process around?
We could do image to text.
Why not try doing text to
images and see how it works?
It was a more difficult task.They didn’t want
to retrieve existing images
the way google search does.
They wanted to generate entirely novel scenes
that didn’t happen in the real world.
So they asked their computer model for
something it would have never seen before.
Like all the school buses you've seen are yellow.
But if you write “the red or green school bus”
would it actually try to generate something green?
And it did that.
It was a 32 by 32 tiny image.
And then all you could see is like a
blob of something on top of something.
They tried some other prompts like “A herd
of elephants flying in the blue skies”.
“A vintage photo of a cat.”
“A toilet seat sits open in the grass field.”
And “a bowl of bananas is on the table.”
Maybe not something to hang on your wall
but the 2016 paper from those researchers
showed the potential for what might
become possible in the future.
And uh... the future has arrived.
It is almost impossible to overstate how far
the technology has come in just one year.
By leaps and bounds.
Leaps and bounds.
Yeah, it's been quite dramatic.
I don’t know anyone who
hasn’t immediately been like
“What is this? What is happening here?”
Could I say like watching waves crashing?
Party hat guy.
Seafoam dreams.
A coral reef.
Cubism.
Caterpillar.
A dancing taco.
My prompt is Salvador Dali painting
the skyline of New York City.
You may be thinking, wait
AI-generated images aren’t new.
You probably heard about this generated portrait
going for over $400,000 at auction back in 2018.
Or this installation of morphing portraits,
which Sotheby’s sold the following year.
It was created by Mario Klingemann, who
explained to me that that type of AI
art required him to collect a specific dataset of
images and train his own model to mimic that data.
Let's say, Oh, I want to create landscapes,
so I collect a lot of landscape images.
I want to create portraits,
I trained on portraits.
But then the portrait model would not
really be able to create landscapes.
Same with those hyper realistic
fake faces that have been plaguing
linkedin and facebook – those come from a
model that only knows how to make faces.
Generating a scene from any combination of words
requires a different, newer, bigger approach.
Now we kind of have these huge
models, which are so huge that
somebody like me actually cannot train
them anymore on their own computer.
But once they are there, they are
really kind of— they contain everything.
I mean, to a certain extent.
What this means is that we can now
create images without having to actually
execute them with paint or
cameras or pen tools or code.
The input is just a simple line of text.
I'll get to how this tech works later in the video
but to understand how we got here,
we have to rewind to January 2021
When a major AI company called Open AI announced
DALL-E – which they named after these guys.
They said it could create images from text
captions for a wide range of concepts.
They recently announced DALLE-2, which promises
more realistic results and seamless editing.
But they haven’t released
either version to the public.
So over the past year, a community of
independent, open-source developers
built text-to-image generators out of other
pre-trained models that they did have access to.
And you can play with those online for free.
Some of those developers are now working
for a company called Midjourney,
which created a Discord community with bots that
turn your text into images in less than a minute.
Having basically no barrier to entry to
this has made it like a whole new ballgame.
I've been up until like two
or three in the morning.
Just really trying to change things, piece things together.
I've done about 7,000 images. It’s ridiculous.
MidJourney currently has a wait-list for
subscriptions, but we got a chance to try it out.
"Go ahead and take a look."
“Oh wow. That is so cool”
“It has some work to do. I feel like it can
be — it’s not dancing and it could be better.”
The craft of communicating
with these deep learning
models has been dubbed “prompt engineering”.
What I love about prompting
for me, it's kind of really
that has something like magic where you have to
know the right words for that, for the spell.
You realize that you can refine
the way you talk to the machine.
It becomes a kind of a dialog.
You can say like “octane render blender 3D”.
Made with Unreal Engine...
...certain types of film lenses and cameras...
...1950s, 1960s...
...dates are really good.
...lino cut or wood cut...
Coming up with funny pairings, like a Faberge Egg McMuffin.
A monochromatic infographic poster about
typography depicting Chinese characters.
Some of the most striking images
can come from prompting the model
to synthesize a long list of concepts.
It's kind of like it's having a very strange
collaborator to bounce ideas off of and get
unpredictable ideas back.
I love that!
My prompt was "chasing seafoam dreams,"
which is a lyric from the Ted Leo and the Pharmacists' song "Biomusicology."
Can I use this as the album cover for my first album? "Absolutely."
Alright.
For an image generator to be able to
respond to so many different prompts,
it needs a massive, diverse training dataset.
Like hundreds of millions of images scraped from
the internet, along with their text descriptions.
Those captions come from things like the alt text
that website owners upload with their images,
for accessibility and for search engines.
So that’s how the engineers
get these giant datasets.
But then what do the models actually do with them?
We might assume that when
we give them a text prompt,
like “a banana inside a snow globe from 1960."
They search through the training data
to find related images and then copy
over some of those pixels. But
that’s not what’s happening.
The new generated image doesn’t
come from the training data,
it comes from the “latent space”
of the deep learning model.
That’ll make sense in a minute, first
let’s look at how the model learns.
If I gave you these images and told you to match
them to these captions, you’d have no problem.
But what about now, this is
what images look like to a
machine just pixel values for red green and blue.
You’d just have to make a guess, and
that’s what the computer does too at first.
But then you could go through
thousands of rounds of this
and never figure out how to get better at it.
Whereas a computer can eventually figure out a
method that works- that’s what deep learning does.
In order to understand that this arrangement
of pixels is a banana, and this arrangement
of pixels is a balloon, it looks for metrics that
help separate these images in mathematical space.
So how about color? If we measure
the amount of yellow in the image,
that would put the banana over here and the
balloon over here in this one-dimensional space.
But then what if we run into this:
Now our yellowness metric isn’t very
good at separating bananas from balloons.
We need a different variable.
Let’s add an axis for roundness.
Now we’ve got a two dimensional space with the
round balloons up here and the banana down here.
But if we look at more data we may come
across a banana that’s pretty round,
and a balloon that isn’t.
So maybe there’s some way to measure shininess.
Balloons usually have a shiny spot.
Now we have a three dimensional space.
And ideally, when we get a new image we
can measure those 3 variables and see
whether it falls in the banana region
or the balloon region of the space.
But what if we want our model to recognize,
not just bananas and balloons,
but…all these other things.
Yellowness, roundness, and shininess don’t
capture what’s distinct about these objects.
That’s what deep learning algorithms do
as they go through all the training data.
They find variables that help improve their
performance on the task and in the process,
they build out a mathematical space
with way more than 3 dimensions.
We are incapable of picturing multidimensional
space, but midjourney's model offered this and I like it.
So we’ll say this represents the latent space of
the model. And It has more than 500 dimensions.
Those 500 axes represent variables that
humans wouldn’t even recognize or have
names for but the result is that
the space has meaningful clusters:
A region that captures the essence of banana-ness.
A region that represents the textures
and colors of photos from the 1960s.
An area for snow and an area for globes
and snowglobes somewhere in between.
Any point in this space can be thought
of as the recipe for a possible image.
The text prompt is what navigates us to that
location. But then there’s one more step.
Translating a point in that mathematical
space into an actual image involves a
generative process called diffusion.
It starts with just noise and then,
over a series of iterations, arranges pixels
into a composition that makes sense to humans.
Because of some randomness in the process,
it will never return exactly the
same image for the same prompt.
And if you enter the prompt into a
different model designed by different
people and trained on different
data, you’ll get a different result.
Because you’re in a different latent space.
No way. That is so cool. What the heck? The brush
strokes, the color palette. That’s fascinating.
I wish I could like — I mean he’s dead,
but go up to him and be like, "Look what I have!"
Oh that’s pretty cool. Probably the
only Dali that I could afford anyways.”
The ability of deep learning to extract
patterns from data means that you can copy an
artist’s style without copying their images,
just by putting their name in the prompt.
James Gurney is an American illustrator who
became a popular reference for
users of text to image models.
I asked him what kind of norms he would like
to see as prompting becomes widespread.
I think it's only fair to
people looking at this work
that they should know what the prompt
was and also what software was used.
Also I think the artists should be allowed
to opt in or opt out of having their work
that they worked so hard on by hand be used
as a dataset for creating this other artwork.
James Gurney, I think he was a
great example of being someone
who was open to it, started
talking with the artists.
But I also heard of other artists
who got actually extremely upset.
The copyright questions regarding
the images that go into training the
models and the images that come out
of them…are completely unresolved.
And those aren’t the only questions
that this technology will provoke.
The latent space of these models contains some
dark corners that get scarier as
outputs become photorealistic.
It also holds an untold number
of associations that we wouldn’t
teach our children but that
it learned from the internet.
If you ask an image of the CEO,
it's like an old white guy.
If you ask for images of
nurses, they're all like women.
We don’t know exactly what’s in the
datasets used by OpenAI or Midjourney.
But we know the internet is biased toward
the English language and western concepts,
with whole cultures not represented at all.
In one open-sourced dataset,
the word “asian” is represented first
and foremost by an avalanche of porn.
It really is just sort of an infinitely complex
mirror held up to our society and what we
deemed worthy enough to, you know, put
on the internet in the first place and
how we think about what we do put up.
But what makes this technology so
unique is that it enables any of
us to direct the machine to
imagine what we want it to see.
Party hat guy, space invader, caterpillar, and a ramen bowl.
Prompting removes the obstacles between ideas
and images, and eventually videos, animations,
and whole virtual worlds.
We are on a voyage here, that
is it's a bigger deal than
than just like one decade or the
immediate technical consequences.
It's a change in the way humans imagine,
communicate, work with their own culture
And that will have long range,
good and bad consequences that we
we are just by definition, not going to
be capable of completely anticipating.
Over the course of researching this video
I spoke to a bunch of creative people
who have played with these tools.
And I asked them what they think this all means for people who make a living making images.
The human artists and illustrators and designers and stock photographers out there.
And they had a lot of interesting things to say.
So I've compiled them into a bonus video.
Please check it out and add your own thoughts in the comments. Thank you for watching.
Please play the YouTube video first