Google Brain's New Model Imagen is Even More Impressive than Dall-E 2

If you thought Dall-e 2 had great results, wait until you see what this new model from Google Brain can do.

Dalle-e is amazing but often lacks realism, and this is what the team attacked with this new model called Imagen.

They share a lot of results on their project page as well as a benchmark, which they introduced for comparing text-to-image models, where they clearly outperform Dall-E 2, and previous image generation approaches. Learn more in the video...

►Read the full article: https://www.louisbouchard.ai/google-brain-imagen/
►Paper: Saharia et al., 2022, Imagen - Google Brain, https://gweb-research-imagen.appspot.com/paper.pdf
►Project link: https://gweb-research-imagen.appspot.com/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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if you thought dali 2 had great results

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wait until you see what this new model

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from google brain can do delhi is

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amazing but often lacks realism and this

0:10

is what the team attacked with this new

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model called imogen they share a lot of

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results on their project page as well as

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a benchmark which they introduced for

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comparing text to image models where

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they clearly outperformed daily2 and

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previous image generation approaches

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this benchmark is also super cool as we

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see more and more text to image models

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and it's pretty difficult to compare the

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results unless we assume the results are

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really bad which we often do but this

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model and le2 definitely defied the odds

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tldr it's a new text-to-image model that

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you can compare to dali to with more

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realism as per human testers so just

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like dali that i covered not even a

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month ago this model takes texts like a

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golden retriever dog wearing a blue

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checkered barrette and a red dotted

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turtleneck and tries to generate a

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photorealistic image out of this weird

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sentence the main point here is that

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imogen can not only understand text but

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it can also understand the images it

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generates since they are more realistic

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than all previous approaches of course

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when i say understand i mean its own

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kind of understanding that is really

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different than ours the modal doesn't

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really understand the text or the image

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it generates it definitely has some kind

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of knowledge about it but it mainly

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understands how this particular kind of

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sentence with these objects should be

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represented using pixels on an image but

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i'll concede that it sure looks like it

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understands what we send it when we see

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those results obviously you can trick it

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with some really weird sentences that

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couldn't look realistic like this one

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but it sometimes beats even your own

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imagination and just creates something

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amazing still what's even more amazing

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is how it works using something i never

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discussed on the channel a diffusion

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model but before using this diffusion

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model we first need to understand the

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text input and this is also the main

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difference with dali they used a huge

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text model similar to gpt3 to understand

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the text as best as an ai system can so

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instead of training a text model along

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with the image generation model they

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simply use a big pre-trained model and

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freeze it so that it doesn't change

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during the training of the image

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generation model from their study this

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led to much better results and it seemed

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like the model understood text better so

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this text module is how the model

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understands text and this understanding

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is represented in what we call encodings

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which is what the model has been trained

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to do on huge datasets to transfer text

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inputs into a space of information that

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it can use and understand

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now we need to use this transform text

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data to generate the image and as i said

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they used a diffusion model to achieve

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that but what is a diffusion model

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diffusion models are generative models

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that convert random gaussian noise like

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this into images by learning how to

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reverse gaussian noise iteratively they

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are powerful models for super resolution

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or other image to image translations and

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in this case use a modified unit

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architecture which i covered numerous

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times in previous videos so i won't

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enter into the architectural details

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here basically the model is trained to

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denoise an image from pure noise which

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the orient using the text encodings and

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a technique called classifier free

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guidance which they say is essential and

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clearly explained in their paper i'll

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let you read it for more information on

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this technique so now we have a model

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able to take random gaussian noise and

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our text encoding and denoise it with

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guidance from the text encodings to

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generate our image but as you see here

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it isn't as simple as it sounds the

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image we just generated is a very small

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image as a bigger image will require

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much more computation and a much bigger

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model which are not viable instead we

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first generate a photorealistic image

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using the diffusion model we just

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discussed and then use other diffusion

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models to improve the quality of the

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image iteratively i already covered

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super resolution models in past videos

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so i won't enter into the details here

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but let's do a quick overview once again

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we want to have noise and not an image

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so we cover up this initially generated

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low resolution image with again some

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gaussian noise and we train our second

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diffusion model to take this modified

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image and improve it then we repeat

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these two steps with another model but

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this time using just patches of the

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image instead of the full image to do

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the same upscaling ratio and stay

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computationally viable and voila we end

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up with our photorealistic high

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resolution image

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of course this was just an overview of

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this exciting new model with really cool

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results i definitely invite you to read

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their great paper for a deeper

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understanding of their approach and a

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detailed results analysis

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and you do you think the results are

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comparable to delhi too are they better

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or worse i sure think it is dally's main

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competitor as of now let me know what

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you think of this new google brain

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publication and the explanation i hope

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you enjoyed this video and if you did

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please take a second to leave a like and

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subscribe to stay up to date with

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exciting ai news if you are subscribed i

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will see you next week with another

amazing paper