Dear viewer, these subtitles were generated by a machine via the service YouTube and therefore are (very) buggy. If you are capable, please help us to create good quality subtitles: https://c3subtitles.de/talk/3201 Thanks!
[Music]
and I'm very happy uh and honored that
these guys here are on this show today
I'm very I'm looking forward to it
myself and actually they're going to
show us What machines do when they start
to communicate with each other and
influence each other I'm looking forward
to what T
tunl and
leonan uh here to have to talk to us I
have to show to us I expect some nice
images as well I expect some nice
insights in what these machines all do
to each other please give a warm welcome
to our new guest
[Music]
here
hello hello can you hear me oh so um
today we're going to be talking about uh
self canalizing AI which is a fancy word
for recursion and we are trying to
approach different artistic strategy to
try to understand expose exploit somehow
um text to image models uh specifically
stable diffusion but we did have some
experience in all other like Tex models
so let's start with something like this
uh what happens when AI confronted an AI
generated
image we start by having image to image
model running and then once we put the
output we put it in again as an input
and we see something like
this it's colorful random purplish
pattern uh and then we start wondering
like what causes this like what why does
it go pink why does it have a certain
pattern and does it have something to do
with the initial image we input and then
we did more test we had a lot of stock
footages and then as the initial
image we ended up having more and more
purple or more and more purple-ish
pattern and this begins our journey to
try to understand what it it actually is
stabil Fusion or this image generation
model like how is the architecture
behind it and what causes this problem
or not even a problem it's aesthetically
nice looking but what exactly happens
there yeah but before we uh dive into um
stable diffusion and um other models we
first want to introduce ourselves my
name is Leon Chu I'm a computer
scientist and media artist based in
Cologne uh and I mainly focus on um
stuff like no promp in like prompting
without prompts or um feedback systems
um in artificial intelligence and data
visualization these are my main points
and my name is tining l uh I'm also a
media artist and like half half bake
programmer uh I want to start by briefly
mention my own project I'm doing a lot
of beach image that were generated based
on different uh various artificial
intelligence model and throughout
history we see all these interesting
social media Beach image that ended up
having clear sky white sand nice looking
beautiful structure this is caused by
human-made bias because uh all these
speeches that are online are usually the
one that you see on social media so we
started by talking about biases and this
is a really interesting paper uh trying
to deconstruct all the biases that
happen into the machine learning models
it's important to notice that we often
time um think of biases being result by
data but it's not only data because
throughout the whole training process
some other bias could have happened
during everything like you could see
that if you train the data in a certain
way it will result a different bias if
you generate the image or generate the
text in a certain way you will also
result in a certain
bias so and uh why do we talk about AI I
mean biases there's lots of talk about
it um um please don't quote us on this
this is humoristic um um it just sums up
uh the development of AI and how it the
hype is perceived and we just want to
emphasize that even though we are
talking about AI all the problems that
we show of biases of recursive pipelines
that feed from one another are problems
that happen in all of software
development so it's not only AI but the
interesting part for AI is we are right
now it feels like we are in this top
peak of the high AI hype and um it
sometimes feel like there's this
linearity of progress that we started
with like I don't know um the touring
test in the 1940s and now we have chat
gbt and it feels like this is this one
continuous uh upwards thing this is what
lots of marketers want us to believe but
what we want to emphasize is that for us
uh especially like the text to image
models it feels purely accidential it
feels like we look at one very
particular branch in these in the tree
of papers and development we look at
this one particular thing that kind of
works right now and like probably 5
years ago if we would have held this
talk we would probably have talked about
G like generative adversaria networks
but right now we are talking about
diffusion models cuz these are
everywhere and this is right now the hot
thing but kind of
accidential uh we want to start by
explaining our artistic concept we want
tode uh I Luc is I'm sitting in the room
which he did a a sound work he had a
tape machine and then recorded in I am
sitting in the room and then do a play
back and has another tape machine to
record and then play back again and
doing this back in forth in the end you
get something like a wide noisy sound or
like some people might have tested maybe
to have a printer you print out a random
image and then you scan it again print
it again scan it again print it again
you ended up having a wide noise
whatever that is image is usually white
and we were thinking about um Shannon's
communication theory about how noise
Source was affecting uh the in the
output Source throughout the input and
the really interesting thing is that
when aier was trying to measure the
sound or the Reverb of the space he's
using feedback mechanism and we're
trying to use the same mechanism to
understand this space which is the
stable diffusion or the latent space or
the embedding space we deconstruct the
whole pipelines and try to run in
different uh components with our
different feedback
strategies so before we show you more
colorful images we we have to talk about
uh what kind of uh stab diffusion is or
what this is I mean lots of you have
probably used it and it feels like it is
this black box where you input text and
then you get an image out of it and it
feels like when I first heard of it I
was thinking about it as this large
neural network that has like text input
and image output but actually if you
look into the pipelines and these are
like different representations of the
pipeline what stab diffusion is actually
doing on the left a mathematical one in
the middle one that I found on hugging
phas which is like this flow chart on
the right it's code there's this one one
particular part that's very interesting
for us because it's a loop structure so
like the the the process of text to
image it's not this one to one relation
but there's this thing that goes in a
loop and it uh goes towards something so
there's already feedback in these
systems so us using them uh as feedback
systems is kind of ironic because we use
a feedback system against itself um and
one other thing that's very interesting
uh about this pipeline is you see it's
very complicated it's all things are are
complicated that's true but like there's
different um steps in this pipeline that
come from different models so there's
multiple machine learning models working
together so it's not only this one text
to image model like you sometimes are
ought to believe there's actually like
very very different parts of the machine
that go together and there's this great
um U thing on Wikipedia where you can
see in this graphics on the lowest layer
on The Orange Box these are the things
that we know these are on the
application layer the things you can use
in your browser these are the apps where
you input text and you get an image out
of it it's like mid Journey stable
diffusion Del I think you you probably
heard of them but the funny thing is it
also shows which generative models are
used and also what is used to um to
embed the text like how the text is
processed and what you can see is like
all of the things we use right now they
go back to this one model called clip
and when we first saw this we were like
what is clip we kind of have to to look
at it because it's in every single
application that we use right now for
text to image and the other thing that's
very interesting is if you look in the
top most row there's also data set and
there's like one one data set not one
data set but there's like Lion 400 m but
it's variations of the same data set so
there seems to be one model that's used
throughout most text to image models and
also one kind of data set or one
provider of data sets and this should
make you curious is to see what is in
this data set and also what is this
model clip that is used
throughout trying to go back to
reinforce the idea we were mentioning
about accidental thing that happens in
the current AI image generation model is
this models are the backbone of all the
models we're using right now and this is
accidential they could have been other
models that could be the backbone of
anything so what what this means is that
if we investigate this model enough the
bias we see in inside could be the bias
of most of the image generation
model so we want to we we dug a bit
deeper into what exactly clip is uh clip
to simplify it you could input a line of
text or keyword and then an image and
inside of this model it finds the
correlation or the distance or a
similarity score between this text and
this image as an example we input the
keyword happy and then we input two
faces these faces were created by this
person does not exist it's the AI
generated faces 5 years ago which is was
not the whole diffusion thing which face
has the higher correlation or looks
happier vote
now left right we ended up seeing that
the right side of the uh picture has a
higher similarity score but what about
these two
images
left left right right
right well the right side is the happier
image but what does it actually mean
like what does it have to do what what
does this score means like now we have a
model where we input a keyword and then
an image but we ended up having a a
number and this number um brings the
possibility that we could calculate
distances correlations relations
probability and this became the backbone
of all the image generation model and
it's
creepy uh just a bit uh context to it a
clip was created by open AI but before
that there was a paper about a medical
journal that they were trying to find
correlations between symptoms and x-ray
images it makes a lot of sense because
they want to understand does this uh X
image has a higher correlation higher
possibility that it ended up in certain
symptoms and thus open the ey create a
clip they inputed tons of data a tons of
image and tons of text at the it's
important to notice at the time they
were not planning to do an image
generation model this wasn't the plan
this model was built to do image
recognition and it's a new way to do
image recognition because it's not like
finding certain um components or finding
certain details and image it's just
feeding image and finding correlations
to the text they tokenize the text and
also toonize the image and then just
find a certain relationship in this
high-dimensional space and one of the
use case which some of you might already
experience is the not safe for work
filter in stabil diffusion they also use
clip at the end point once you generate
an image they will run through clip and
then it's really funny that they use
this 17 keywords and once one of the
keyword has passes the threshold it will
trigger the not sa for work filter and
this is extremely prob atic because it
we as we said this keyword is just a
mathematical representation of the
correlation it doesn't really
semantically means anything and for
example we will see a lot of false
positive like the photo of Donald Trump
jumping into the pool will resulted in a
trigger from the filter and some random
um abstract image May somehow has some
components inside when going through
clip it also triggers the not safe for
work
filter so we start by doing some
experiments we were thinking of if we
put a lot a lot a lot of this person
does not exist and then we run through
the scoring system with the happy image
will we ended up finding the happiest
person from this person does not
exist um
okay
so uh
yeah
quick
demo so on the left side you will see
the picture that we in real time
downloaded from this person does not
exist and on the left side this is
currently the lowest score of the photo
and this is the higher
score and you see that throughout a lot
of different iteration we will ended up
sort of seeing the most happiest person
right now we have someone like this and
the most unhappy it's not the saddest
it's unhappy or like the farest or
having the most distance between this
image the keyword
happy okay oh there's another one oh
okay let's continue uh okay so now we go
into a really interesting question so
now we input the keyword happy but what
if we put other keywords and then we put
different stuffs and then also run
through stock footage and we have our
friend pedrol
here it it's funny because it doesn't
make sense but it somehow also makes
sense I
guess but just to keep in mind that this
is a really really important point that
we're trying to express this clip model
is the foundation of most of the image
generation model so if if we have a
woman as a higher score in nurse
definitely in the image generation model
we could have that generated and this is
the danger that we are trying to propose
and also at the same time there are a
certain artistic quality that they're
looking for like a grapest not art at
all but a random firework has a higher
correlation to the keyword art and thus
is the bias that we were
proposing and then we ended up inputting
different keywords and then do like a SX
uh comparison between them this random
AI generated face has a really high
similarity score to Aboriginal
Australian and also this image of schnit
has a higher uh probability between
Chinese and European it makes sense for
us but this is us putting our own bias
onto this image and onto this
mathematical space I think one thing
that's very important this list of
ethnicities we we we took it from the US
Navy so it's not our list we didn't make
a list of ethnicities we just Googled
for one and then the US military surely
has one so we just used the US military
ethnicity list that you can when you
enlist cross in which ethnicity you
are and we put other
stuff it it's funny because as a human
mind it makes somehow some sense but
this is the bias that we want to talk
about we are inputting our own bias onto
these machine and onto these
models so we put put our own faces also
and we're trying to we put a list of
different countries and then we ended up
having the higher score of which name of
the country that has a higher
correlation to
us and it's funny because we found
something called
Chad oh I
mean uh it's we have
to go back to talk about again that it's
not a homophone because it's really
interesting in that mathematically this
keyword chat has double meaning but at
the same time it's us as a human to
understand what that means for the math
part for the latent space they don't
really
know and we try to contextualize it if
we input the keyword with a sentence in
the beginning it says person from The
Country Canada or person from the
country blank then we ended up having a
more proper result but by proper I mean
it's
biased also we put
[Music]
food it's good that we use our own
face but that's the point we want to try
to emphasize like what what exactly is
this model doing and this is the
backbone of
everything so now we understand that if
we have enough faces and then if we go
through enough iteration
we ended up like finding the happy happy
face but the most happy face again is
just a higher similarity score to the
keyword happy but how about generating a
happy
face yeah thanks uh if you remember when
we tried in our live demo where we tried
to find the the happiest and the
furthest away from happiest person we
use this person does exist.com as our
generator which just gives you random
faces it's admittedly a very stupid
generator it just gives you a random
face and then we look is it the higher
score and then we keep it or we throw it
away so we already almost built our own
text to image model because we can input
any text and then with lots of lots of
iterations we will it will find a face
that kind of fits our description but
like the interesting part is also like
we can generate this image on the way by
by using kind of the similar structure
so kind of we build our own text to
image model but stab diffusion is a bit
more complicated and um we shortly have
to go over this slide again uh it I
always find it very funny to look at all
of these different representations cuz I
mean we can look at all of them and not
understand them I don't understand them
but at least I found the parts that kind
of made sense and are like similar
things in these different
representations and there's like
different steps that are kind of
important or like happen in in the
pipeline that I could kind of make out
in all of these representations and the
first one is that we need to embed the
text which just means you type in a
textt and you get a very high
dimensional Vector that you then can
calculate with uh this is uh What uh
clip is used for that so that's exactly
what Ting showed you beforehand and uh
then uh the second part is like the
actual diffusion where the the image
gets generated and this happens from
noise so we start with like some very
colorful image and then it slowly over
time generates the image uh trying to
follow the text that we input like
mathematically and then in the end CU I
lied to you all of this doesn't happen
with pixels it happens in some abstract
space that we can't see it has to uh be
converted back into something we can so
into pixel space and then we have the
finished image that we can actually see
and I want to sort of emphasize again on
the embedding part this is a really
important thing that we need to think
about uh how do we embed the keyword
happy into a number because we don't as
a human we have emotions and these
emotions are currently being embedded
into a string of number this really
doesn't make sense it don't really align
with our sentience and but this model
Works solely because of that because we
could embed different types of keyword
into numbers and then it generates the
image yeah that's uh very important once
you have typed in anything all the
meaning is gone it's all actually bits
and bites so um all the meaning we put
into it if we use text to image models
is like the things we perceive so we put
meaning back into it again and this is
like this process it's also called
reverse diffusion um what's very
interesting is like the current image
the one on the left is like the the
current state of the image it's an
iterative process if you ever used
prompting you can type in steps that's
like the steps of diffusion it does then
the thing in the middle this is actually
the thing the the model learns which is
just noise how to generate noise and the
Right image is then the what it predicts
uh the image will look like so it's
basically the left image plus the noise
in the middle equals the Right image and
you can see like over time it slowly
slowly slowly goes towards the concept
of dog and this is like happening in a
loop so like step for step it goes it
generates new noise to go towards the
keyword for example dog and this is very
important like stab diffusion does not
generate images this sounds weird but
it's actually a very very sophisticated
noise generator so it generates the
thing in the middle and the interesting
thing about the architecture that they
used because we already heard that clip
comes from like the medical field the
unet structure that generates this noise
in the middle also comes from the
medical field and was used to segment
also x-ray images so maybe the most
advancement was not open my ey it was
medicine after all and um what's
interesting in this process is that uh
you can also say how much should it go
towards dog for example uh and you can
also um in the code just like comment
out the line like just don't go towards
anything just do your thing just denoise
it like like just add noise over it and
see where it ends up and this is what we
did here we just cut out the part where
it goes towards a concept and let it
like Den noise itself and see where it
ends up and it's very funny because you
can still make out things in there even
though there's like no guidance in there
at all and it probably this is probably
the rawest representation of like the
Laten base of the raw data in there
probably the couple down there in the
middle is somewhere in the data set and
if there's no guidance it can just
reproduce these patterns and things that
are normally not seen you you can also
do the inverse so say uh instead of
going a very small number towards cat
you can also like do it times a thousand
and get like the cattiest cat uh which
is even for us not the cattiest cat but
here you see from left to right top to
bottom we just increased this go more
towards the keyword cat and you can see
very fast it gets outside of our realm
of understanding what a cat
is but now back to how do did we do
these red images cuz this this is the
kind of the one of the main topics of
our talk is you probably have used um
image to image which is the same as text
to image just instead of an of an uh
text you UT an image and um what you can
do with this is you can give it an image
and then you describe it in a prompt I
want to make this drawing into some
fantasy landscape and then you get a
fantasy landscape but what we did is we
um we input an image also stock images
again and we didn't put in any prompt so
we just said like do do do your thing do
your thing without anything and we wrote
a small little Library that's very
shitty so please don't read the code and
um just did this in a loop so we said uh
take this stock image uh do your thing
then we took the output and we put it
back in again and I mean we already
spoiled you about this it turned very
pink very quickly and we were like okay
how can this be is this like a floating
Point error do we have to I don't know
change like some parameters around so we
just tried again with different models
and uh it turns out different models
these are like in the order of their
release stab diffusion 1.5 2.1 XL and XL
turbo like the main versions and you can
see all of the models converge into
something but they all have their own
favorite color so choose your spirit
stable diffusion model now um so stab
diffusion 1.5 goes into this weird
colorful purple patterns like stab
Fusion 2.1 more like primary colors sdxl
straight lines all the way and
architecture and stab diffusion XL is
like this
turquaz
photography I can't even describe it and
um we were still curious like okay this
is still like some calculation error we
doing something wrong so we tried it
again there is the strength parameter so
when you do image to image you can say
how much of the original image you want
to keep and uh so we tried it like 0.5
is like the the base thing so it does
like 0.5 your image and then noise on it
and we tried it until 0.8 and uh even at
0.8 you have to imagine it generates
lots of new stuff it still has their
favorite things and patterns that it
kind of converges to and funnily enough
stab Fusion XL just likes architecture
so much it can't get away from the
windows and um stabil Fusion 1.5 it lost
the pink and turned black and white but
it's still the same kind of patterns and
interestingly stab Fusion XL you see
this bird and this uh butterfly they
come all the time like they probably
it's some XL training problem but it's
something that repeats over and over
again so we were looking more into the
code and I I told you about before that
all of this stuff doesn't happen in
pixel space so it's not really pixels
that we're dealing with it's like
encoded into some mathematical space and
then it's decoded again into something
we can see so what we did is we used
this encoder which should do nothing so
you take an image you encode it into
this latent thing and then you decode it
again and we did this in a loop as well
so we put the output back in again
decode encode decode encode and um
funnily enough yeah sure it was not able
to reproduce the image perfectly which
mathematically probably makes sense but
still result in these very beautiful
organic patterns that look similar to
the stab diffusion 1.5 I have to say but
still have like their own distinct noisy
quality and kind of show probably how
this and what this uh autod decoder does
you can see there's like smoing
happening so it's probably like
summarizing pixels
together and um to kind of now rule out
that it's like a stable Fusion thing we
we Ed Del as well so we used stable
fusion and Del and they playing shipost
so we let stab Fusion generate one image
we give it to Del let it generate an
image and we put it back in again and we
do this in a loop so they communicate
with each other with images and funnily
enough it does not turn into pink noise
but you can still see we started with
random photography stock images again it
still devolves into some noise so maybe
it's like float input errors in both or
there might be something that's inherent
to these diffusion models that uh have
the inability to reproduce images like
they were inputed and um the other thing
that we did we we didn't stop there we
used the stab diffusion upscaler which
does uh when you give it an image it
just upscales it four times and then you
have a higher image uh and what we did
is we um upscaled an image then we down
sampled it again like manually not with
AI we just put it in Photoshop and made
it small again and then we upsampled it
again and then we did this also in a
loop and funnily enough we tried like
different methods because we were sure
okay it's some calculation error but no
no matter the thing uh no matter the
parameters no matter the input image it
still gave us these very beautiful yet
uh
non non-existent patterns um which just
shows that it was unable to reproduce
the image completely um which also still
has like these very organic qualities
which probably say something about the
algorithm behind
it the other thing we did is like we we
tried to play shipost again and we had
like two models against each other again
this time it was text to image and image
to text um text to image we all know but
there's also the inverse which is image
captioning actually like the research
term and what it does is you give it an
image and it tries to describe it in
this version we used gpt2 I know it's
very old and clip um and we started with
an image you see here like the um the
photography of like the ocean and then
we made a text out of it so it made a
large body of water with few surface and
then it we turned that back into an
image again and we wanted to see where
this ends up so we did this also in a
loop and um we found that even though
it's not pink patterns there are still
like some things it likes a Lot Like
Trains
um um yeah
[Applause]
[Music]
trains so it gone stuck on trains it got
stuck on trees then fire hydrants
probably because they have this very
nice red color um brick walls with
clocks on it lots of clocks than dogs
sure the internet has has lot of dogs uh
also benches it likes benches
and um yeah lots of lots of things but
still like the same reoccurring things
it was always the same uh things
repeating over and over again then it
went back from cats to dogs to fire
hydrant so it was always repeating the
same things oh there's bear and big
walls I forgot um this is probably a
thing with the image captioning cu the
image captioning is gbt2 it's probably
not too good at like captioning images
we are not sure but there is something
uh reductionistic about these systems
that's kind of funny um we then kind of
found a model that was able to reproduce
an input image almost perfectly it was
realistic
vision5 or something so like some very
specific model and some very specific um
parameters and if we put that in we were
able to like almost reproduce the image
you can try for yourself like the left
image is the first image and the in the
middle one is the is the new image that
it was able to reproduce and on the
right just for visualization purposes I
put the difference between the two for
us I would say it's kind of the same
image but the interesting thing is uh if
you do this in a loop and we did it like
16,000 times in a loop um you can see
that from time to time it still changes
a lot it goes uh towards horses uh it
turns black and white it um will also
show lots of wedding photography and
architecture and I'm sad we don't have
time for this this goes on for like 10
minutes but you can just believe me it
will not
uh
[Music]
it will not stop at one thing and we we
looked at it in uh in slow motion to see
like where come does the faces come from
where where does it come from and you
can actually see if you look closely
enough that sometimes a face uh
disappears into a lantern for example
and um going back to the thing again of
like the mathematically equivalency oh
there he's gone they're back
[Music]
again
so um people disappearing and appearing
like the change is that there's like
even in the image there's like a
mathematically ambivalencia at one point
between for example a lantern and a phas
and it doesn't make the distinction
between the two so from one frame to
another it's a face and then it keeps a
face and then the face grows a little
bit and funnily enough the method we
used here is kind of also how these
animation Frameworks that you see online
work they kind of also work like this on
the right you see a map uh and what I
tried to visualize here was um um going
from U um a program from David McLure
who took like I think 12 million images
of a data set and also used clip and put
them into a map and I wanted to see like
where it travels so I try to find the
closest point to this uh image on the
left and you see it's like super chaotic
so the machine sees like very different
things from frame to frame and it's it's
not for us not very easy to follow so so
we kind of have to go back to the ba
data again cuz now we talked about the
algorithm and um it it kind of shows
okay there is problems with the
algorithms but maybe it's also a data
thing so we looked into like um what
stable Fusion is trained on and it's
trained uh on um uh a data set from lion
lion is like a nonprofit maybe someone
from Lion is here let's talk later that
um take um common crawl which is like
just slice from the internet downloaded
and they go through these slices of the
internet like these archives and they
look for images and their alt text like
their description um and then they
collect them and curate large very large
data sets and in this data set you then
have just the URL a description of the
text and then some parameters and
funnily enough you don't have to read
all this on the left the only important
thing here is that you see that there's
like even the training process is like a
very complicated thing with feedback in
it so there's like different steps with
like different parameters so it's not
this thing we just throw the data at it
and then it learns it's this very
iterative process of adding layers of
layers of data after each other and one
thing that's very interesting is like
these parameters here they are called
pun saave and aesthetic score and pun
saave is just the not safe for work
score so they also filter out some not
safe for work images and we showed you
in the beginning how well that works so
probably there's some stuff missing in
the data and they also used something
called aesthetic score and uh aesthetic
scoring what this actually means like
Lion themselves this is no front to Lion
I'm sure the closed Source people do the
same thing so at least we can read about
what this does it is the question how
much do you like this image on a scale
from 1 to 10 this is the aesthetic score
but they didn't do this by hand because
what the largest data set has 5 billion
images so they trained an AI to do it so
they have an AI that actually takes one
image and gives you a score between 1 to
10 and um how they trained is is they
they took like U um images from DP
challenge which is like a photography
forum and also from um some Discord
where people could rate their own stable
diffusion Generations from 1 to 10 and
they trained uh a newal net on it to
decide for a score and this here this is
a website that is public uh we can also
yeah and we can do this live we can
aesthetically predict ourselves um which
just gives you a score okay the sad
thing is if you are below five you not
in stable diffusion so uh I'm not in
stable diffusion but it's probably the
lighting um and we have to remember this
also uh uses clip so it's also it kind
of works with clip as well so you have
to also consider the whole image like
it's the lighting it's all the thing
it's the contrast if it's if it's drawn
in waterc color and stuff cuz if we look
at the actual slices from these data
sets we can see in the lowest uh tier of
this data there's like lots of logos and
memes and stuff so it kind of learned
something and but if we go to the very
high tiers we have to scroll some time
um you will see lots of artworks like
most of them are copyrighted this should
be obvious and if you go to the very
highest uh score it's like mostly stuff
painted in watercolor or acrylic and
stuff so it learns something but maybe
only the correlation between aesthetic
score and watercolor paintings but it is
at least something um the that this is
problematic I mean I don't have to I
don't think I have to point that out but
um we can just look at it cuz I I took a
subset of lion the top
0.05 most aesthetic images and I also
used um the code that I showed you
before um into a map so we can shortly
take a glimpse into this data space this
is a whole another talk so I have to go
very
fast so this what you see here is like
600 uh 50,000 images and you can color
them by different parameters you can for
example look at like not sa for work
images um color scale is not the best
blue means High pink means low and
there's one very interesting correlation
that happens here is uh if we look at
most of the very high um scores for um
not safe for work it's mostly like
female stars so it probably learned
something very sexist that um is
definitely in all of the data sets so
probably because the correlation between
the words that we showed you before like
uh nudity Etc is because it learns from
the internet has probably picked up on
these uh being used derogatory for
female stars so it kind of has learned
this probably so um I can yeah the all
the blue Parts is mostly uh female stars
and the other very important uh thing or
very interesting thing is that I looked
in one of these clusters um and found
some Star Wars images also some
memes all of these images are downloaded
like live CU if I would download them
beforeand it would probably be illegal
so we are all illegal together um and I
was wondering I was like wondering
around the Star Wars cluster here and
then I saw this weird outline here which
was all um images of The French Chef
Paul bukus which is like a famous very
famous chef from France and I was
wondering why is he so close to Star
Wars is this uh some
weird correlation that I have not picked
up on and there actually is an
explanation we can explain it right now
with the mouse cursor so we start with
like some Star Wars over here so um Star
Wars is now owned by Disney and Disney
has like um Disneyland and in Disneyland
there's galaxies Galaxy world I think
it's called which is like the Star Wars
part and uh but also in Disney World um
they opened a new part of this theme
park um which was about the movie
Ratatouille and uh in there there's a
restaurant and in this restaurant on the
opening uh who was cooking there it was
Paul bis so um the the
[Music]
[Applause]
yes so somehow clip has picked up on
this very weird coincidence of like
relation between Star Wars and Paul buus
so the next time you do stable diffusion
and you generate a Star Wars image uh if
there's in the background someone
looking like Paul bukus you know know
why this is the case maybe um yeah I
think uh yeah we're kind of coming to
the end of our talk um because um we
talked now about like this is kind of
silly like we used uh algorithms against
what they're usually meant to do um
which is also very valid you should
always try to do that like misuse is
also a very uh good use but this is
actually like something we should worry
about and uh I found one example it's
called open Journey um it's a stable
diffusion version that learns um from
exclusively or is fine-tuned exclusively
on mid-journey images so it's one stable
diffusion version that learns from
another generative AI so kind of these
feedback loops kind of are happening and
there's actually like some research
about it and they call it now model
collapse it's very new and it's a new
fancy term it's what happens when
artificial systems especially generating
ones learn from also at artificially
generated stuff so artificially
generated images for example or also for
artificially generated texts for large
language models for example so this is
actually these feedback systems that are
now we used in a silly fun way it might
actually be something um that uh could
happen in the near future so we want to
go back to the original thing that we
have with the red image and this is a
really important question as a metaphor
or even as a hypothesis at the time it
was early this year when they Fusion 1.4
was on and once we were generating image
we heard that oh it it could be a
possibility that all the people were
generating image they posted online and
then throughout the years they will be
trained back again and if this iteration
happens again and again will we possibly
ended up in the internet where
everything is purple we don't know could
be but this is more or less a hypothesis
or a metaphor that we're thinking about
because all as we previously mentioned
in clip or in all this all these
different models we all have have
certain biases whether it's visually
appearing or it's not but if we keep on
working with these model somewhere we
ended up in a certain I don't know the
green pattern the architecture pattern
in our internet images and this is how
we want to end our talk here so thank
you
[Applause]
[Music]
[Applause]
yeah
[Music]
super super work guys so really
fantastic I guess we will have some
questions here isn't it yeah no shall I
do
one so how did you do this with the live
images that you just
captured the live images you just
captured live
image all I guess so what uh we're going
to have some questions here okay good
number one hello and thank you uh I was
just curious um on the feedback loops
that you did running um images through
the same model again and again I I only
saw that you did this with like general
purpose image generating models did you
also try and do this with like luras or
fine tunings or models that were made to
do specifically cats or something mm I
mean uh actually the the movie that you
saw is kind like the model we used
realistic Vision I would say is kind of
a fine tun model because it's already
fine tuned on these very high realistic
stock footage images and this is
probably why you see lots of like brides
and grooms and stuff like this CU it's
kind of fine-tuned towards these images
so um it it it kind of showed very
similar images to the images it was
trained on so probably uh we didn't do
it like for a very fine Tri model but it
would probably also repeat uh the same
things over and over again so it would
be fair to say that you find like what
you'd expect there probably yeah yeah
thank you good uh here number three
please yeah we at the Cow Congress here
right you remember probably the 80s with
the cowos theory um M BR up men all this
and there was this attractor concept so
you have an attractor and that sounds
pretty much like this did you think
about the connection between Chaos
Theory attractors there and what you
do kind of uh when we first uh talked
about these Concepts that it would fall
into we we we we talked to them we
called them sinks like something it
would go in and like never get out of it
again CU in one of the first things we
tried it was would always end up in
images of toilets and bathrooms with
sinks so we kind of made the connection
between syn and syn so we kind of
imagined it as this whole in the
possibility space that it would fall
into and never get out of it again but
um throughout the experiments we're not
too sure if it's really an attractor
that it can't get off of or if it's just
more like a computational artifact so
we're not really sure if there is like
we missing out on stuff and it really
falls into a hole or if it's just a
plane like it's always there once you
are uh below certain fresh it will
always be this yeah but we kind of
thought about uh something similar
yeah here number one pleas yeah
fantastic talk thank you very much um
you talked about what the algorithm
might pick up uh and looked into the
data have you looked into what the
encoding before that was like jpeg for
example could it be like the
picked up like glitches and um uh like
the weird encoding reminds me of like EG
uh or jpeg colors um so have you look
into that uh it's a really good question
yeah the the thing is that uh it it did
change because once we were when we were
trying to investigate on the clip model
we did found that they had a certain way
of doing tokenization based on image so
it ended up having all into embedding
space they made their own tokenization
in their model but we're not really able
to crack it open so yeah and for the
image part uh my first guess was when it
turned red I thought it was like some
encoding thing like some the red Channel
kind of I don't know is maybe more
important and that's why I don't know
maybe in the variable out encoder for
example red seems to be more important
and that's why it turns into red kind of
and maybe this comes from we I thought
it was come from some color uh input or
some um format but I mean we tried with
different models and it turned out to be
different colors and different patterns
and I think it's more on a higher level
of like abstraction cuz like I said
before it doesn't really calculate in
pixels all this diffusion part happens
in a latent space already so uh the jpeg
artifacts they would also have to be
encoded into this thing maybe there is
like some latence that correlate to like
these artifacts on the on a certain
layer but um maybe
not yeah all right since our signal
Angel is uh disappearing okay number one
here uh thanks for your
talk uh that was my highlight talk
actually
and so one understanding question this
um image to text correlation model this
um clip is this still actually used or
is it like a
um something that was like a version
before or is it still used I I did
understand sense it's still in use and
that's the crazy part because it works
so people just continue using it and
they were like fine-tuning or make it uh
make it more efficient on all these
application wise model but the backbone
of clip because it takes too much
manpower to actually created that so it
ended up kind of always there there is a
we didn't really mention it but there's
two different uh variation which is clip
uh it's a open AI product but there's
also something called open clip and open
clip did what's kind of funny is that
they were trying to make it as much as
clip itself but it ended up having the
same structure but it's the bias we
talked about or the issue we we talked
about it's always also appear in both of
models okay
thanks good we continue here oh hi there
great talk great nle um in the talk you
showed how you rerun the encoding
process uh the encoding between actual
RGB pixels and this internal Vector
space and what happens to like the
visual image um have you also tried
running the uh generation process like
Let It think without copying back and uh
into RGB space like running in cycle the
generation in the uh internal space yeah
I actually thought of it but we never
did it but we I had the same thought
because I was thinking okay maybe it's a
quantization error it's something like
on this way from latent space outside
but then I thought again for um for our
purposes we kind of wanted to show that
you can do this like drag and drop an
image it this works with real images for
the idea of what is happening in the
future our future will happen in pixel
space because this idea of model
collapse and all these things they will
happen on the pixel level CU all our
images are in pixel space that we
publish on social media so we were get
back to
you uh very soon and um so we thought
about it but we didn't we never did it
okay thanks thanks all right signal
Angel was there some news from out of
space no yes oh yes then this was the
yes yes we do actually have two
questions I'll ask one first is the tool
that used for exploring the latent space
available or would you make it available
to the
public uh it's some uh code that's uh a
Frankenstein code from like different
GitHub repositories so I would have to
check the licenses again and also I'm
not sure like uh right now copyright
wise I'm not sure if I'm allowed to do
this cuz like all of these images are
copyrighted so when you hover with the
mouse over it the actual image get gets
downloaded to your computer so I'm not
really sure so for legal reasons no but
maybe when uh I looked more into it you
can find it online
um I will tweet about
it good was there one question you had
too you said yes the other question is
that the internet would like to know as
more and more content is ordered or AI
generated and put on the internet and
maybe feeds into future training data if
there's any way to protect future
training from that there was uh it's
also happening early this year there was
some proposal through uh both stab
fusion and a think M Journey that they
add a hidden layer of Watermark it's
another research topic and we did
another talk a long time ago uh it
didn't really work that well because
people tend to download their image and
then they change format and then they
crop it they resize it and then
Watermark didn't really work that well
but I think there research that are
doing right now just to add more water
marks or like some hidden layers into
the image itself to let some of the
models recognize it when they're doing
training yeah but there's one
interesting thing about digital
watermarking if you want to um U not be
digital waterm Mark you can just take
your image and use another AI to like
reproduce it to
99.9% and the digital water mark will
pretty sure be gone so I mean um I think
then we are in the situation that you
have with hacking in general like with
video games for example where you have a
new patch of a new version and then you
have some detector and then you have a
new version so I don't think like
digital watermarking will not solve uh
this
problem an open world please there um so
I'm bit tired but the one of the
patterns that emerg looks a little bit
like uh the reaction diffusion patterns
and activate the inhibitor so maybe you
recreated some dynamical system like
that uh definitely the upscaling one um
I don't know if we can go to definitely
is reaction diffusion and I actually
this is this is the why we have this
slide with the different methods um Cu
uh if you see here uh I know we skipped
over it pretty fast but this one we use
different methods for like downscaling
the image cuz if you think about
downscaling it could be the diffusive
part of an reaction diffusion exactly
and this is what we thought as well so
we used nearest neighbor we used all the
different interpolators that you can use
and also used none so we used nearest
neighbor um uh so it still emerged as
like these reaction diffusion patterns
so I it's probably a reaction diffusion
system because there's some diffusion
when you down scale it but um yeah and
also with the conlusion uh like
you get similar things if you iterate
those yeah okay yeah
definitely I regret to say this but this
is one of the last talks of this
Congress oh my God thank you guys thank
you T shun Lou
and for this fantastic uh presentation
[Applause]
really
[Music]
oh