OpenAI's New Model is Amazing! DALL·E 2 Explained Simply

Last year I shared DALL·E, an amazing model by OpenAI capable of generating images from a text input with incredible results. Now is time for his big brother, DALL·E 2. And you won’t believe the progress in a single year! DALL·E 2 is not only better at generating photorealistic images from text. The results are four times the resolution!

As if it wasn’t already impressive enough, the recent model learned a new skill; image inpainting.

DALL·E could generate images from text inputs.

DALL·E 2 can do it better, but it doesn’t stop there. It can also edit those images and make them look even better! Or simply add a feature you want like some flamingos in the background.

Sounds interesting? Learn more in the video!

►Read the full article: https://www.louisbouchard.ai/openais-new-model-dall-e-2-is-amazing/
►A. Ramesh et al., 2022, DALL-E 2 paper: https://cdn.openai.com/papers/dall-e-2.pdf
►OpenAI's blog post: https://openai.com/dall-e-2
►Risks and limitations: https://github.com/openai/dalle-2-preview/blob/main/system-card.md
►OpenAI Dalle's instagram page: https://www.instagram.com/openaidalle/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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last year i shared dolly an amazing

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model by openai capable of generating

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images from a texan foot with incredible

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results now it's time for his big

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brother dolly too and you won't believe

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the progress in a single year dolly 2 is

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not only better at generating

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photorealistic images from texts the

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results are four times the resolution as

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if it wasn't already impressive enough

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the recent model learned a new skill

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image in painting delhi could generate

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images from text inputs dolly 2 can do

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it better but it doesn't stop there it

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can also edit those images and make them

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look even better or simply add a feature

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you want like some flapping goes in the

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background this is what image and

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painting is we take the part of an image

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and replace it with something else

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following the style and reflections in

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the image keeping realism of course it

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doesn't only replace the part of the

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image at random this will be too easy

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for openai this in-painting process is

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also text guided which means you can

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tell it to add a famine go here there or

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even there

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before diving into the nitty-gritty of

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this newest dahle model let me talk a

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little about this episode sponsor

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weights and biases if you are not

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familiar with weight and biases you are

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powerful feature to either add quick

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comments on an experiment or create

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polished pieces of analysis reports can

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also be used as dashboards for reporting

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a smaller subset of metrics than the

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main workspace you can even create

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public view-only links to share with

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anyone easily capturing and sharing your

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work is essential if you want to grow as

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an ml practitioner which is why i

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recommend using tools that improve your

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work like weights and biases just try it

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with the first link below and start

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sharing your work like a pro

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now let's dive into how dolly 2 can not

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only generate images from text but is

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also capable of editing them indeed this

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new in-painting skill the network has

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learned is due to its better

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understanding of concepts and the images

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themselves locally and globally what i

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mean by locally and globally is that

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dahle 2 has a deeper understanding of

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why the pixels next to each other has

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these colors as it understands the

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objects in the scene and their

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interrelation to each other this way it

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will be able to understand that this

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water has reflection and the object on

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the right should be also reflected there

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it also understands the global scene

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which is what is happening just like if

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you were to describe what is going on

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when the person took the photo here

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you'd say that this photo does not exist

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obviously or else i'm definitely down to

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try that if we forget that this is

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impossible you'd say that the astronaut

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is riding a horse in space so if i were

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to ask you to draw the same scene but on

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a planet rather than in free space you'd

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be able to picture something like that

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since you understand that the horse and

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astronaut are the objects of interest to

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keep in the picture this seems obvious

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but it's extremely complex for a machine

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that only sees pixels of colors which is

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why dahli 2 is so impressive to me but

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how exactly does the model understand

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the text we send it and can generate an

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image out of it well it's pretty similar

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to the first model i covered on the

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channel it starts by using the clip

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model by openai to encode both a text

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and an image into the same domain a

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condensed representation called a latent

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code then it will take this encoding and

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use a generator also called a decoder to

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generate a new image that means the same

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thing as the text since it's from the

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same latent code so dali 2 has two steps

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clip to encode the information and the

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new decoder model to take this encoded

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information and generate an image out of

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it these two separated steps are also

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why we can generate variations of the

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images we can simply randomly change the

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encoded information just a little making

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it move a tiny bit in the latent space

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and it will still represent the same

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sentence while having all different

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values creating a different image

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representing the same text as we see

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here it initially takes a text input and

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encodes it what we see above is the

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first step of the training process where

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we also feed it an image and encode it

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using clip so that images and text are

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encoded similarly following the clip

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objective then for generating a new

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image we switch to the section below

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where we use the text encoding guided by

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clip to transform it into an image ready

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encoding this transformation is done

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using a diffusion prior which we will

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cover shortly as it is very similar to

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the diffusion model used for the final

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step finally we use our newly created

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image encoding and decode it into a new

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image using the diffusion decoder a

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diffusion decoder or modal is a kind of

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model that starts with random noise and

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learns how to iteratively change this

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noise to get back to an image it learns

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that by doing the opposite during

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training we will feed it images and

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apply random gaussian noise on the image

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iteratively until we can't see anything

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other than noise then we simply reverse

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the model to generate images from noise

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if you'd like more detail about this

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kind of network which are really cool i

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invite you to watch this video i made

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about them and voila this is how dali 2

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generates such high quality images

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following text it's super impressive and

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tells us that the model does understand

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the text but does it deeply understand

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what it created

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well it sure looks like it it's the

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capability of impainting images that

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makes us believe that it does understand

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the pictures pretty well but why is that

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so how can it link a text input to an

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image and understand the image enough to

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replace only some parts of it without

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affecting the realism this is all

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because of clip as it links a text input

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to an image if we encode back our newly

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generated image and use a different text

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input to guide another generation we can

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generate the second version of the image

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that will replace only the wanted region

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in our first generation and you will end

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up with this picture unfortunately the

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code isn't publicly available and is not

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in their api yet the reason for that as

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per openai is to study the risks and

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limitations of such a powerful model

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they actually discuss these potential

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risks and the reason for this privacy in

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their paper and in a great repository i

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linked in the description below if you

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are interested they also opened an

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instagram account to share more results

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if you'd like to see that it's also

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linked below i loved dally and this one

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is even cooler

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of course this was just an overview of

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how dahli2 works and i strongly invite

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reading their great paper linked below

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for more detail on their implementation

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of the model i hope you enjoyed this

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video as much as i enjoyed making it and

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i will see you next week with another

amazing paper thank you for watching