Introducing NVIDIA's EditGAN: Alter Images Instantly via Quick Sketches

Have you ever dreamed of being able to edit any part of a picture with quick sketches or suggestions? Or maybe you wanted to change specific features like the eyes, eyebrows or someone in an image, or even the wheels of your car? Well, it is not only possible, but it is now easier than ever with this new model called EditGAN, and the results are really impressive!

Control any feature from quick drafts, and it will only edit what you want keeping the rest of the image the same! SOTA Image Editing from sketches model based on GANs by NVIDIA, MIT and UofT. Watch more results and learn how it works in the video!

►Read the full article: https://www.louisbouchard.ai/editgan/
► Paper: Ling, H., Kreis, K., Li, D., Kim, S.W., Torralba, A. and
Fidler, S., 2021, May. EditGAN: High-Precision Semantic Image Editing.
In Thirty-Fifth Conference on Neural Information Processing Systems.
► Code and interactive tool (arriving soon): https://nv-tlabs.github.io/editGAN/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/

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have you ever dreamed of being able to

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edit any part of a picture with quick

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sketches or suggestions well it's not

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only possible but it has never been

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easier than now with this new model

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called edit gun and the results are

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really impressive you can basically

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improve or mimify any picture super

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quickly indeed you can control whatever

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feature you want from quick drafts and

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it will only edit the modifications

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keeping the rest of the image the same

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control has been sucked and extremely

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challenging to obtain with image

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synthesis and image editing ai models

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like guns you can see how having extra

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control is useful for image editing and

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how it improves the quality of the work

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you create when running machine learning

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projects the quality of the work you

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produce is directly correlated with the

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quality of your tools and the level of

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control they give thankfully for us it's

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easier to take control of your machine

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learning projects using this episode's

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sponsor weights and biases by tracking

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all of the input hyper parameters output

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metrics and any insights that you or

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your team have you know that your work

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is saved and under control one aspect

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that i love for teams is weights and

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biases reports i love how i can easily

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capture all of my projects charts and

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findings in reports that i can share

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with my team and get feedback the charts

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are interactive and tracked with weight

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and biases so i know my work is

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reproducible i feel lucky that i get to

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spend time trying to make research look

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simple and clear for you all and that

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weights and biases is trying to do the

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same with their platform i'd love for

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you to check them out with the first

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link below because they are helping me

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to continue making these videos and grow

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this channel

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as we said this fantastic new paper from

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nvidia the university of toronto and mit

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allows you to edit any picture with

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superb control over specific features

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from sketch inputs typically controlling

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specific features requires huge data

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sets and experts to know which features

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to change within the model to have the

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desired output image with only the

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wanted features changed instead it again

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learns through only a handful of

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examples of labeled images to match

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segmentation to images allowing you to

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edit the images with segmentation or in

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other words with quick sketches it

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preserves the full image quality while

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allowing a level of detail and freedom

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never achieved before this is such a

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great jump forward but what's even

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cooler is how they achieve that so let's

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dive a bit deeper into their model first

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the model uses talgen 2 to generate

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images which is the best image

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generation model available at the time

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of the publication and is widely used in

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research i won't dive into the details

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of this model since i already covered it

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in numerous videos with different

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applications if you'd like to learn more

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about it instead i will assume you have

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a basic knowledge of what style gun 2

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does take an image encode it into a

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condensed subspace and use a type of

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model called a generator to transform

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this encoded subspace into another image

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this also works using directly encoded

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information instead of encoding an image

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to obtain this information what's

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important here is the generator as i

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said it will take information from a

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subspace often referred to as latent

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space where we have a lot of information

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about our image and its features but the

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space is multi-dimensional and we can

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hardly visualize it the challenge is to

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identify which part of the subspace is

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responsible for reconstructing which

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feature in the image this is where

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editgan comes into play not only telling

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you which part of the subspace does what

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but also allowing you to edit them

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automatically using another input a

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sketch that you can easily draw indeed

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it will encode your image or simply take

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a specific latent code and generate both

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the segmentation map of the picture and

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the picture itself this means that both

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the segmentation and images are in the

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same subspace by training a model to do

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that and it allows for the control of

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only the desired features without you

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having to do anything else as you simply

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need to change a segmentation image and

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the other will follow the training will

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only be on this new segmentation

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generation and the style gun generator

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will stay fixed for the original image

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this will allow the model to understand

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and link the segmentations to the same

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subspace needed for the generator to

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reconstruct the image then if trained

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correctly you can simply edit the

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segmentation and it will change the

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image accordingly edit can will

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basically assign each pixel of your

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image to a specific class such as head

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ear eye etc and control these classes

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independently using masks covering the

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pixels of other classes within the

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latent space so each pixel will have its

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label and edit gun will decide which

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label to edit instead of which pixel

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directly in the latent space and we

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construct the image modifying only the

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editing region and voila by connecting a

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generated image with a segmentation map

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edit gun allows you to edit this map as

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you wish and apply these modifications

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to the image creating a new version of

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course after training with these

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examples it works with unseen images and

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like all guns the results are limited to

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the kind of images it was trained with

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so you cannot use this model on images

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of cats if you trained it with images of

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cars still it's quite impressive and i

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love how researchers try to provide ways

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to play with gans intuitively like using

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sketches instead of parameters the code

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isn't available for the moment but it

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will be available soon and i'm excited

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to try it out this was just an overview

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of this amazing new paper and i will

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strongly invite you to read their paper

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for a deeper technical understanding let

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me know what you think and i hope you've

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enjoyed this video as much as i enjoyed

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learning about this new model thank you

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once again to weights and biases for

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sponsoring the video and to you that is

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still watching see you next week with a

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very special and exciting video about

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the subject i love

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[Music]

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you