-: So you have a prompt and it's
a pretty flowery prompt here.
You have a lot of words and we're not
a hundred percent sure whether we need all of these words.
And obviously in Midjourney there's no real limit
to how many tokens you can have,
but it can, I think, add a lot of noise to the image,
if you are pushing the model
in too many different directions
and maybe not all of these tokens are necessary,
you could be getting random results
or like unusual results because
you're including a lot of things
that aren't really contributing a lot, but adding noise.
Okay, so I'm just gonna copy this
and then I'm gonna say shorten,
and this is a technique that I haven't actually seen
that many people use, but it is pretty amazing.
So if you just hit enter, I haven't seen
any other model that has this.
Maybe it'd be possible to build for stable diffusion,
but what it does is it chooses
what are the important tokens and it tells you
here which ones are important.
So graffiti style mural is really important.
So clever is more important than slide.
That's interesting. And then dachshund.
It doesn't need dog showcasing Banksy.
And then it's stencil. It doesn't need technique, right?
It doesn't need set against.
It just needs brick and background.
It actually knows, because it is the model,
it can give you the attention that's paid to each token
and make a subjective decision about
what should be included in the prompt.
And then it gives you this pretty cool thing here,
which is it just slowly narrows it down, deletes words.
Then this is just deleting all the unnecessary words
and then cuts more and more away
until literally if you just put in like mural,
dachshund, Banksy, brick, then
you get something pretty good,
even though you haven't used all of these tokens.
So this is just an interesting concept.
One thing I do a lot is I will use this feature
and then copy the prompt across to stable diffusion.
I know that's not quite exactly transferrable,
but it is useful to know, at least in general,
like what types of words are triggering
for this type of model.
All right. The other thing you can do is
you can click on one of these and it will run that prompt.
But there's one more little thing
I wanna show you, which is show details.
And this is really exciting because
it gives you the numbers, right?
It actually gives you the numbers.
You can see there's a little chart here,
the dachshund and is very important.
Banksy and the mural, but then where as
like showcasing as not as important.
So that's how it makes these decisions.
You can look and say, so I thought the word subtly,
for example, would be important,
but it's zero, point, zero, one
in terms of attention paid to that token.
So yeah, really not worth it.
The other thing is it tells you
a little bit how things are split up.
So saying and social. So that is interesting
because I've used the words stencil, art,
technique, and social commentary.
Like I thought that it would split.
So it's like stencil, art, technique,
and then social commentary would be separate,
but it's actually paired and social together.
So I might be getting unusual results because of that.
Right, so it's really important to know that
and to think about that when
you are starting to split these prompts up
and putting them in different sections.
There's just really good intelligence.
All right, so just out of interest, let's run number five
and see what we get back, and hit submit
and see if we get like a similar image,
even though we've drastically shortened the prompt.
While this is running out, I just wanna say as well,
one thing this teaches me is just that
it is not like a LLM, right?
When you're prompting for images,
it doesn't understand like the words necessarily.
It's just grabbing the individual tokens more than anything.
You might not actually be like using natural language
to describe things like I did here where I said,
and social commentary or a contrasted urban brick.
You could literally just list words,
I think, and get the similar results.
Here we go. We actually got some pretty cool results here.
Although it did get a little bit messed up here,
and that's pretty amazing though.
When you think we went from
all these words down to just four key words.
That's still a pretty amazing result.