Google's LUMIERE AI Video Generation Has Everyone Stunned | Better than RunWay ML?

Summary

Google has unveiled Lumiere, an advanced AI text-to-video model that translates text prompts into high-quality, coherent videos. Lumiere goes beyond text-to-video to animate images, create video-in-painting effects, and generate consistent shots using a spacetime architecture. Research suggests these models may be developing an internal representation of 3D scenes despite only seeing 2D images. Lumiere outperforms other models like Pika and Gen2 in metrics like text alignment and video quality. This technology could empower everyday creators to make Hollywood-style films with AI. The rapid improvements suggest this is an exciting time for aspiring AI cinematographers.

Takeaways

• 😲 Google unveils new AI model Lumiere for generating realistic videos from text and images
• 🎥 Lumiere allows text-to-video, image-to-video, stylized video generation, video animation, and more
• 🌄 Researchers optimized Lumiere for temporal consistency across video frames
• 🔍 Study investigates whether diffusion models learn deep representations or just surface statistics
• 💡 Evidence suggests models develop some innate sense of 3D geometry and scene composition
• 😎 Lumiere outperforms state-of-the-art video AI models ImageN, Gen2, and others
• ⏰ Video quality has drastically improved over the past year thanks to AI advancements
• 🎞 Everyday creators may soon produce Hollywood-style films using AI visuals and voices
• 🚀 'World models' that simulate environments could be the next evolution of video AI
• 📈 Expect rapid improvements in coherence and realism of AI-generated video in the near future

Q & A

What is Lumiere and what are its capabilities?

Lumiere is Google's latest AI text-to-video model. It can translate text prompts into video, animate existing images, create video in the style of an image/painting, and fill in missing sections of an image with video.

How does Lumiere compare to other text-to-video AI models?

According to the paper, Lumiere performs better than other state-of-the-art models like Pika and Gen2 in terms of temporal consistency, text alignment to prompt, and user preference.

How does the SpaceTime architecture used in Lumiere differ from previous approaches?

Whereas previous models generate frames sequentially, Lumiere's SpaceTime architecture generates the full video duration at once, improving global temporal consistency.

What evidence suggests AI models may be learning more than surface statistics?

The Beyond Surface Statistics paper showed Lumiere develops internal representations related to scene geometry and depth despite only seeing 2D images during training.

What are some potential applications of Lumiere?

Lumiere could be used for video stylization, creating CGI and special effects, generating storyboards, converting image collections into video, and more.

How might Lumiere impact filmmaking and content creation?

Lumiere may allow everyday creators to produce Hollywood-quality video using AI, opening up new genres of AI-assisted filmmaking.

What are general world models, and why are they the next step for AI?

General world models are AI systems that simulate entire environments and physics. This will allow for more realistic video and better robotics through a deeper understanding.

How has AI-generated video quality improved over the past year?

Video quality has improved drastically, with more temporally consistent objects and scenes. Compare today's smooth output to distorted legacy examples from a year ago.

What role might creative professionals play in this new era of AI-generated content?

Humans are still needed to provide creative vision and high-level direction. AI will assist with the technical execution, amplifying human creativity.

What developments are coming next for AI-generated video and imagery?

Higher video resolution, longer duration, more photorealism, and tools to easily control and direct the AI are all likely next steps.

Outlines

00:00

😯Introducing Lumiere, Google's new text-to-video AI

This paragraph introduces Lumiere, Google's new text-to-video AI model. It highlights Lumiere's core capability of generating video from text prompts and also allowing animation of existing images. Examples are shown of text-to-video, image-to-video, stylized video generation, video stylization, cinemagraphs, and video in painting. The consistency of the generated videos is noted. An overview of the Spacetime diffusion model used by Lumiere is provided.

05:00

🎥Lumiere enables easy video production and new creative possibilities

This paragraph discusses the potential of Lumiere to transform video production, allowing everyday people to create Hollywood-style videos with AI-generated footage and voices. It also covers some background research into how neural nets generate images, suggesting they may be learning more than just surface statistics.

10:01

👀Debates continue about how generative AI models create content

This paragraph analyzes debates within the AI research community about whether generative models like Lumiere simply memorize pixel correlations between inputs and outputs or if they develop some deeper understanding. An experiment showing a model recreating 3D scene aspects without being explicitly trained to do so is covered.

15:03

😎Lumiere advances the state of the art in consistency and quality

This paragraph evaluates Lumiere's video quality and consistency compared to other leading models like Imagen, Gen-2, and anime-diffusion. Quantitative analysis and side-by-side examples highlight Lumiere's superior performance across text-to-video and image-to-video tasks.

20:05

🤖Simulating entire worlds may be the next frontier

The final paragraph examines the idea of developing general world models to move beyond isolated video clips toward AI systems that simulate fuller environments, enabling the generation of more realistic and complex videos. The rapid recent progress of AI video generation capabilities is also highlighted.

Mindmap

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Keywords

💡Lumiere

Lumiere is described as Google's latest AI tool, a text-to-video AI model that translates text into video. This technology signifies a leap in AI capabilities, allowing for the animation of existing images, video inpainting, and the creation of specific animation sections within images. The script illustrates Lumiere's ability to produce videos from text prompts, such as animating flags, dogs with headphones, and even astronauts on Mars, showcasing its potential to revolutionize video production and creativity.

💡SpaceTime diffusion model

The SpaceTime diffusion model underpins Lumiere's video generation capabilities. It's a sophisticated AI architecture that enables the creation of realistic, coherent motion in videos. Unlike previous models that process frames sequentially, this model considers the entire temporal duration of the video from the outset, ensuring consistency and continuity across frames. This concept is pivotal for understanding Lumiere's advancement over existing AI video technologies.

💡Temporal consistency

Temporal consistency refers to the uniformity and steadiness of visual elements throughout the duration of a video. In the context of Lumiere, it highlights the AI's ability to maintain consistent appearance and behavior of subjects across different frames, an essential factor for creating realistic videos. This contrasts with earlier models where objects could change unexpectedly from one frame to the next, detracting from realism.

💡Image to video

Image to video conversion is a feature of Lumiere that animates still images into dynamic video sequences. Examples from the script include animating a bear walking in New York or Bigfoot traversing the woods. This capability demonstrates how AI can breathe life into static images, creating engaging and visually captivating content from simple pictures.

💡Video inpainting

Video inpainting is a process where missing parts of a video frame are filled in or reconstructed using AI. Lumiere's application of video inpainting showcases its ability to intuitively guess and replicate what might occupy these voids, such as adding green leaves to a scene based on the context. This highlights the AI's advanced understanding and predictive capability regarding video content.

💡Stylized generation

Stylized generation refers to Lumiere's ability to apply specific styles or artistic effects to videos, based on a target image or painting. This allows for the creation of videos not just in realistic styles but also in artistic or abstract aesthetics, demonstrating the flexibility and creative potential of Lumiere for various applications in art and entertainment.

💡Cinemagraphs

Cinemagraphs are still photographs in which a minor and repeated movement occurs. Lumiere's mention of creating cinemagraphs, such as animating smoke from a train, illustrates its nuanced understanding of motion and ability to apply it selectively within a scene to create visually striking effects.

💡General World models

General World models, as discussed in the context of AI development, refer to sophisticated AI systems that build internal representations of environments and simulate future events within those environments. This concept is crucial for understanding the direction in which AI video generation technology, including Lumiere and other platforms like Runway ML, is heading, aiming for more realistic and dynamically coherent video content.

💡Neural Networks

Neural Networks are a core component of AI technologies like Lumiere, simulating the way human brains operate to make decisions and predictions. The script discusses how neural networks are used in AI models to predict and generate video content, even inferring 3D geometries from 2D images, which underscores the complexity and advanced capabilities of current AI video generation tools.

💡AI cinematography

AI cinematography emerges from the script as a concept where AI technologies, like Lumiere, are utilized to create films or videos. It represents a shift in content creation, where AI's role transitions from a tool to an active participant in the creative process, enabling users to produce high-quality, cinematic content without the need for extensive resources or traditional filmmaking techniques.

Highlights

Lumiere is a new AI tool from Google that generates realistic videos from text prompts.

Lumiere allows animating images and creating specific animation sections within images.

Lumiere produces smooth, temporally consistent videos compared to other models.

Lumiere performs image-to-video and text-to-video generation better than leading models.

Lumiere uses a spacetime diffusion model to generate the entire video at once.

Other models struggle with temporal consistency across frames.

Lumiere seems to develop an internal 3D representation despite only seeing 2D images.

Debate continues on whether AI models learn surface statistics or deeper understanding.

RunwayML is working on general world models to improve video generation.

World models simulate environments to create more realistic imagery and motion.

AI-generated video has improved rapidly, from incoherent to lifelike in 1-2 years.

AI tools may enable easy Hollywood-quality movie production at home soon.

AI could help creative people make movies without financial limitations.

Next steps are AI-assisted world building and story generation.

Now is an exciting time to explore AI-generated film as quality quickly improves.

Transcripts

and just like that out of the blue

Google drops its latest AI tool Lumiere

Lumiere is at its core a text to video

AI model you type in text and the AI

neural Nets translate that into video

but as you'll see Lumiere is a lot more

than just text to

video it allows you to animate existing

images creating video and the style of

that image or painting as well as things

like video in painting and creating

specific animation sections within

images so let's look at what it can do

the science behind it Google published a

paper talking about what they improved

and I'll also show you why the

artificial AI brains that generate these

videos are much more weird than you can

imagine so this is lumere from Google

research A Spacetime diffusion model for

realistic video generation we'll cover

SpaceTime diffusion model a bit later

but right now now this is what they're

unveiling so first of all there's text

to video this is the video that are

produced by various prompts like US flag

waving on massive Sunrise clouds funny

cute pug dog feeling good listening to

music with big headphones and Swinging

head Etc snowboarding Jack Russell

Terrier so I got to say these are

looking pretty good if these are good

representations of the sort of style

that we can get from this model this

would be very interesting so for example

take a look at this one astronaut on the

planet Mars making a detour around his

base this is looking very consistent

this looks like a tablet this looks like

a medicine tablet of some sort floating

in space but I got to say everything is

looking very consistent which is what

they're promising in their research it

looks like they found a way to create a

more consistent shot across different

frames temporal consistency as they call

it here's image to video so as you can

see that this is nightmarish but that's

that's the scary looking one but other

than that everything else is looking

really good so they're taking IM images

and turning them into animations little

animations of a bear walking in New York

for example Bigfoot walking through the

woods so these were started with an

image that then gets animated these are

looking pretty good here are the Pillars

of Creation animated right there that's

uh pretty neat kind of a 3D structure

they're showing styliz generation so

using a Target image to kind of make

something colorful or animated take a

look at this elephant right here one

thing that jumps out at me is it is very

consistent there's no weirdness going on

in a second we'll take a look at other

leading AI models that generate video

and I got to say this one is probably

the smoothest looking one here's another

one so as you can see here here's the

style reference image so they want this

style and then they say a bear twirling

with delight for example right so then

it creates a bear twirling with delight

or a dolin leaping out of the water in

the style of this image here's the same

or similar prompts with this as the

style reference now this as a the style

reference I got to say it captures the

style pretty well here's kind of that

neon phosphorus glowing thing and they

introduce A Spacetime unit architecture

and we'll look at that towards the end

of the video but basically it sounds

like it creates sort of the idea of the

entire video at once so while other

models it seems like kind of go frame by

frame this one has sort of an idea of

what the whole thing is going to look

like at the very beginning and there's a

video stylization so here's a lady

running this is the source video and the

various craziness that you can make her

into the same thing with a dog and a car

and a bear cinemagraphs is the ability

to animate only certain portions of the

image like the smoke coming out of this

train this is something that Runway ml I

believe recently released and looks like

Google is hot on their heels creating

basically the same ability then we have

video and painting So if a portion of an

image is missing you're able to use AI

to sort of guess at what that would look

like I got to say so here where the hand

comes in that is very interesting cuz

that seems kind of advanced cuz notice

in the beginning he throws the Green

Leaf in the missing portion of the image

and then you see him coming back to the

image that we can see throwing a green

leaf or two so it makes the assumption

that hey the things there will also be

green leaves interestingly enough though

I do feel like I can spot a mistake here

the leaves that are already on there are

fresh looking as opposed to the cooked

ones like they are on this side so it

knows to put in the green leaves as the

guy is throwing them for them to be

fresh because it matches the fresh

leaves here but it misses the point that

hey these are cooked leaves and these

are fresh but still it's very impressive

that it's able to sort of to sort of

guess at what's happening in that moment

and this is where if you've been

following some of the latest AI research

this is where these neural Nets get a

little bit weird well again come back to

that at the end but how they are able to

predict certain things like what happens

here for example like no one codes it to

know that this is probably a cake of

some sort nobody tells it what this

thing is it guesses from clues that it

sees on screen but how does that is

really really weird let's just say that

this is pretty impressive so here we're

able to change the clothes that the

person is wearing throughout these shots

while you know notice the hat and the

face they kind of remain consistent

across all the shots whereas the dress

is changed based on a text prompt as you

watch this think about where video

production for movies and serial TV

shows Etc where that's going to be in 5

to 10 years will something like this

allow everyday people sitting at home to

create stunning Hollywood style movies

with whatever characters they want

whatever settings they want with'

generated video and AI voices we can

create a movie starting Hugh Hefner as a

chicken for example so really fast this

is another study called Beyond surface

statistics out of hardw so this has

nothing to do with the Google project

that we're looking at but this paper

tries to answer the question of how do

these models how do they create images

how do they create videos as you can see

here it says these models are capable of

synthesizing high quality images but it

remains a mystery how these networks

transform let's say the phrase car in

the street into a picture of a car in a

street so in other words when we type in

this when a human person says draw a

picture of a car in a street or a video

of a car in a street how does that thing

do it how does it translate that into a

picture do they simply memorize

superficial correlations between pixel

values and words or are they learning

something deeper such as the underlying

model of objects such as cars roads and

how they are typically positioned and

there's a bit of a argument going on in

the scientific Community about this so

some AI scientists say all it is is just

sort of surface level statistics they're

just memorizing where these little

pixels go and they're able to kind of

reproduce certain images Etc and some

people say well no there's something

deeper going on here something new and

surprising that these AI models are

doing so what they did is they created a

model that was fed nothing but 2D images

so images of cars and people and ships

Etc but that model it wasn't taught

anything about depth like depth of field

like where the foreground of an image is

or where the background of an image is

it wasn't taught about what the focus of

the image is what a car is ETC and what

they found is so here's kind of like the

decoded image so this is kind of how it

makes it from step one to finally step

15 where as you can see you can see this

is a car so a human being would be able

to point at this and say that's a car

what in the image is closest to you the

person taking the image you say well

probably this wheel is the closest right

this is the the kind of the foreground

this is the main object and that's kind

of the background that's far far away

and this is close right but the reason

that you are able to look at this image

and know that is because you've seen

these objects in the real world in the

3D world you can probably imagine how

this image would look if you're standing

off the side here looking at it from

this direction this AI model that made

this has no idea about any of that all

it's seeing is a bunch of these 2D

images just pixels arranged in a screen

and yet when we dive into try to

understand how it's building these

images from scratch this is what we

start to notice so early on when it's

building this image this is kind of what

the the depth of the image looks like so

very early on it knows that sort of this

thing is in the foreground it's closer

to us and this right here the blue

that's the background it's far from us

now looking at this image you can't

possibly tell what this is going to be

you can't tell what this is going to be

till much much later maybe here we can

kind of begin to start seeing some of

the lines that are in here but that's

about it you you see like the wheels and

maybe you could guess of what that is

but here in the beginning you have no

idea and yet the model knows that

something right here is in the

foreground something's in the background

and towards the end it knows that this

is closer this is close and this is far

this is Salient object meaning like what

is the focus what is the main object so

it knows that the main object is here it

doesn't know what a car is it doesn't

know what an object is it just knows

like this is the the focus of the image

again only towards much later do we

realize that yes in fact this is the car

and so this is the conclusion of the

paper our experiments provide evidence

that stable diffusion model so this is

an image generating model AI although

solely trained on two-dimensional images

contain an internal linear

representation related to scene geometry

so in other words after seeing thousands

or millions of 2D images inside its

neural network inside of its brain it

seems like and again this is a lot of

people sort of dispute this but some of

these research makes it seem like it's

developing its neural net that allows it

to create a 3D representation of that

image even though it's never been taught

what 3D means it uncovers a salent

object or sort of that main Center

object that it needs to focus on versus

the background of the image as well as

information related to relative depth

and these representations emerge early

so before it starts painting the colors

or the little shapes or the the wheels

and the Shadows it first starts thinking

about the 3D space on which it's going

to start painting that image and here

they say these results add a Nuance to

the ongoing debates and there are a lot

of ongoing debates about this about

whether generative models so these AI

models can learn more than just surface

statistics in other words is there some

sort of understanding that's going on

maybe not like human understanding but

is it just statistics or is there

something deeper that's happening and

this is Runway ml so this is the other

one of the leading sort of text 2 image

AI models and you might have seen the

images so as you can see here this is

what they're offering people have made

full movies maybe not hour long but

maybe 10 minutes 20 minute movies that

are entirely generated by AI so as you

can see here it's it's similar to what

Google is offering although I got to say

after looking at Google's work and then

this one Google's does seem just a

little bit more consistent I would say

there seems to be a little bit less

shifting and and shapes going on it's

just a little bit more consistent across

time time and they have a lot of the

same thing like this stylization here

from a reference video to this image

that's like the style reference but the

interesting thing here is this is in the

last few months looks like December 2023

Runway nml introduced something they

call General World models and they're

saying we believe the next major

advancement in AI will come from systems

that understand the visual world and its

Dynamics they're starting a long-term

research effort around what they call

General World models so their whole idea

is that instead of the video AI models

creating little Clips here and there

with little isolated subjects and

movements that a better approach would

be to actually use the neural networks

and them building some sort of a world

model to understand the images they're

making and to actually utilize that to

have it almost create like a little

world so for example if you're creating

a clip with multiple characters talking

then the AI model would actually almost

simulate that entire world with the with

the rooms and the people and then the

people would talk talk to each other and

it would just take that clip but it

would basically create much more than

just a clip like if a bird is flying

across the sky it would be simulating

the wind and the physics and all that

stuff to try to capture the movement of

that bird to create realistic images and

video so they're saying a world model is

an AI system that builds an internal

representation of an environment and it

uses it to simulate future events within

that environment so for example for Gen

2 which is their model their video model

to generate realistic short video it has

developed some understanding of physics

and motion card still very limited

struggling of complex camera controls or

object motions amongst other things but

they believe and a lot of other

researchers as well that this is sort of

the next step for us to get better at

creating video at teaching robots how to

behave in the physical world like for

example the nvidia's foundation agent

then we need to create bigger models

that simulate entire worlds and then

from those worlds they pull out what we

need whether that's an image or text or

a robot's ability to open doors and pick

up objects all right but now back to

Lumiere A Spacetime diffusion model for

video generation so here they have a

number of examples for the text to video

of image to video stylized generation

Etc and so in lumier they're trying to

build this text video diffusion model

that can create videos that portray

realistic diverse and coherent motion a

pivotal challenge in video synthesis and

so the new thing that they introduces

the SpaceTime unet archit tecture that

generates entire temporal duration of

the video at once so in other words it

sort of thinks through how the entire

video going to look like in the

beginning as opposed to existing video

models so other video models which

synthesize distant key frames followed

by temporal super solution basically

meaning they do it one at a time so they

start with one and then create the

others and they're saying that makes

Global temporal consistency difficult

meaning that the object as as you watch

a video of it right it looks a certain

way on the first second of the video but

by second five is just completely

different and so here basically they're

comparing these two videos so imagin and

rs so The Lumiere model as you can see

here here sample a few clips and they're

looking at the XT slice so the XT slice

you can basically think of that as so

for example in stocks you have you know

the price of stock over time right so it

kind of goes like this here the x is the

spatial Dimension so where certain

things are in space on the image versus

T temporal the time so the X here is

basically where we might be looking at

the width of the image for example of

any image in time and T the temporal is

like how consistent is across time so as

you can see hit this green line so we're

just looking at this thing across the

entire image and this is what that looks

like so as you can see here this is

going pretty well and then it kind of

messes up and it kind of gets crazy here

and then kind of goes back to doing okay

whereas in Lumiere it's pretty pretty

good I mean maybe some funkiness right

there in one one frame but it's pretty

good same thing here I mean this is as

you can see here pretty good maybe you

can say that there's a little bit of

funkiness here but overall it's very

good whereas in this image and video I

mean as you can see here there's kind of

like a lot of nonsense that's happening

right and so here you can see like you

can't tell how many legs it has if it's

missing a leg Etc whereas in The Lumiere

I mean I feel like the you know you can

see each of the legs pretty distinctly

and their position and it's remains

consistent across time or at least

consistently easy to see where they are

but I got to say I can't wait to get my

hands on it it looks like as of right

now I don't see a way to access it this

is just sort of a preview but hopefully

they will open up for testing soon and

we'll be able to get our hands on it and

check it out and here interestingly

enough they actually compare how well

their performs against the other

state-of-the-art models in the in the

industry so the two that I'm familiar

with is Pika and genan 2 those are the

two that I've used and they're saying

that their video video is preferred by

users in both text to video and image to

video generation so blue is theirs and

the Baseline is the orange one so it

seems like there are pretty big

differences in every single one this

seems like video quality I mean it beats

out every single other one of these

which which I believe this text

alignment which here means probably how

well the image how true it is to The

Prompt right so if you type in a prompt

how accurately it represents it so it

looks like maybe image is the closest

one but it beats out most of the other

ones by quite a bit and then video

quality of image to video it seems like

it beats them out as well with Gen 2

probably being the next best one and

here they provide a side-by-side

comparison so for example the first

prompt is a sheep to the right of a wine

glass so this is Pika which which not

great CU there's no wine glass here's

Gen 2 consistently putting it on the

left anime diff which just has two

glasses and maybe a reflection of a

sheep image and video same thing so the

glasses on the left zero scope no

glasses that I can see although they

have sheep and of course R so the Lumi

the Google one is it seems like a nail

it in every single one the glass is on

the right although I got to say Gen 2 is

is great although it confused the left

and right but other than that I mean

same if image and video actually

although I feel like Gen 2 the quality

is much better of the sheep cuz that's

you know that's a good-looking sheep I

should probably rephrase that that's a

well rendered sheep how about that

versus imagin I mean that's a weird

looking thing there that could almost be

a horse or a cow if you just look at the

face and Google is again excellent

here's teddy bear skating in Time Square

this is Google this is imag again

weirdness happening there and that's gen

two again pretty good but I mean the the

thing is facing away although here I

just noticed so they they took skating

to mean ice skates whereas here it looks

like these are roller skates skateboard

Etc and so it looks like in the study

they just showed you two to things they

say do you like the left or the right

more based on motion and better quality

well I got to say if you're an aspiring

AI cinematographer then this is really

good news consistent coherent images

that are able to create near lifelike

scenes at this point I mean I'm sure

there's other people that'll complain

about stuff but you got to realize how

quickly the stuff is progressing just to

give you an idea this is about a year

ago or so this is what a I generated

video looked like so can you tell that

is improved just a little bit that's

about a year I'm not sure exactly when

this was done but I'm going to say a

year year and a half ago and I mean this

thing gets nightmarish so when I'm

talking about weird blocky shapes things

not being consistent across scenes like

what are we even looking at

here is this a mouth is this a building

and here's kind of uh something from

about 4 months ago from Pika Labs so as

you can see here it's much better it's

much more consistent right as you can

see here humans again maybe they look a

little bit weird but it's better it can

put you in the moment if you're telling

a story that's not necessarily about

everything looking realistic something

like this can be created pretty easily

and since it's new it's novel people

might be this might be a whole new

movement a new genre of film making

that's new exciting and never before

seen and most importantly it's easy to

create with a you know at home with a

few AI tools and anybody out there with

creative abilities with creative talent

to tell the stories that they have in

their mind without being limited

financially by Capital they're going to

be able to create AI voices they're

going to be able to create AI footage

maybe even have Chad GPT help them with

some of the story writing and once more

the sort of the next generation of

things that we're seeing that people are

working on is things like the similation

where you create the characters and then

you sort of let them loose in a world

they get simulated with these they get

sort of simulated so the stories kind of

play out in the world and then you sort

of pick and choose what to focus on

which scenes and which characters you

want to bring to the front so you

basically act as the World Builder you

build the worlds the characters the

narratives and AI assists you in

creating the visuals the voices Etc and

you can be 100% in control of it or you

can only control the things that you

want and the AI generates the rest so to

me this if you're interested in movie

making and you like these sort of styles

that by the way quickly will become much

more realistic I would be really looking

at this right now because right now is

the time that it's sort of emerging into

the world and getting really good and

it's going to get better by next year

it's going to be a lot

better well my name is Wes rth and uh

thank you for watching