SDEdit Helps Regular People Do Complex Graphic Design Tasks

Say goodbye to complex GAN and transformer architectures for image generation. This new method by Chenling Meng et al. from Stanford University and Carnegie Mellon University can generate new images from any user-based inputs.

Even people like me with zero artistic skills can now generate beautiful images or modifications out of quick sketches. It may sound weird at first, but by just adding noise to the input, they can smooth out the undesirable artifacts, like the user edits, while preserving the overall structure of the image.

So the image now looks like this, complete noise, but we can still see some shapes of the image and stroke, and specific colors. This new noisy input is then sent to the model to reverse this process and generate a new version of the image following this overall structure.

Meaning that it will follow the overall shapes and colors of the image, but not so precisely that it can create new features like replacing this sketch with a real-looking beard. Learn more in the video and watch the amazing results!

►Read the full article: https://www.louisbouchard.ai/image-synthesis-from-sketches/
►My Newsletter (A new AI application explained weekly to your emails!): https://www.louisbouchard.ai/newsletter/
►SDEdit, Chenlin Meng et al., 2021, https://arxiv.org/pdf/2108.01073.pdf
►Project link: https://chenlin9.github.io/SDEdit/
►Code: https://github.com/ermongroup/SDEdit
►Demo: https://colab.research.google.com/drive/1KkLS53PndXKQpPlS1iK-k1nRQYmlb4aO?usp=sharing

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say goodbye to complex GAN and

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transformer architectures for image

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generation

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this new method by channing meng el from

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stanford university and carnegie mellon

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university can generate new images from

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any user based inputs even people like

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me

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with zero artistic skills can now

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generate beautiful images

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or modifications out of quick sketches

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it may sound weird at first but just by

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adding noise to the input

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they can smooth out the undesirable

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artifacts like the user edits

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while preserving the overall structure

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of the image so the image now looks like

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this

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complete noise but we can still see some

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shapes of the image and strokes and

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specific colors

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this new noisy input is then sent to the

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model to reverse this process

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and generate a new version of the image

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following this overall structure

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meaning that it will follow the overall

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shapes and colors

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of the image but not so precisely that

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it can create

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new features like replacing the sketch

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with a real looking beard

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the same way you can send a complete

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draft of an image like this

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add noise to it and it will remove the

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noise by simulating the reverse steps

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this way it will gradually improve the

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quality of the generated image following

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a specific dataset style

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from any input this is why you don't

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need any drawing skills anymore

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since it generates an image from noise

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it has no id and doesn't need to know

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the initial input before applying noise

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this is a big difference and a huge

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advantage compared to other generative

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networks

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like conditional GANs where you train a

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model to go from one style to another

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with image pairs coming from two

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different but related data sets

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by the way if you find this interesting

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don't forget to subscribe like the video

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and share it with your friends or

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colleagues

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it helps a lot thank you this model

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called sd edits

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uses stochastic differential equations

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or sdes

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which means that by injecting gaussian

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noise they transform

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any complex data distribution into a

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known prior

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distribution this known prior

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distribution is seen

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during training and this is what the

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model is trained on to reconstruct the

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image

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so the model learns how to transform

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this gaussian noisy input

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into a less noisy image and repeats this

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step until we have an image

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following the one style this method

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works with whatever type of input

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because if you add enough noise to it

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the image will become so noisy that it

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joins the known distribution

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then the model can take this known

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distribution and

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do the reverse steps denoising the image

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based on what it was trained on

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indeed just like GANs we need a target

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dataset

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which is the kind of data or images we

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want to generate

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for example to generate realistic faces

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we need a data set

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full of realistic faces then we add

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noise to these face

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images and teach the model to denoise

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them iteratively and this is the beauty

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of this model

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because once it has learned how to

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denoise an image we can pretty much do

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anything to the image

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before adding noise to it like adding

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strokes since they are blended within

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the expected image distribution

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from the noise we are adding typically

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editing an image based on

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such strokes is a challenging task for a

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gan architecture

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since these strokes are extremely

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different from the image and from what

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the model has seen

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during training a GAN architecture will

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need two data sets to fix this

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the target data set which will be the

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one we try to imitate and a source data

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set which is the images with strokes

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that we are trying to edit these are

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called paired

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datasets because we need each image to

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come in pairs

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in both data sets to train our model on

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we also need to define a proper loss

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function to train it

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making the image synthesis process very

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expensive and time consuming

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in our case with sd edits we do not need

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any paired data sets since the stroke

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and the image styles are merged

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because of this noise this makes the new

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noisy image part of the known data

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for the model which uses it to generate

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a new image very similar to the training

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data set

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but taking the new structure into

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account in other words

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it can easily take an edited image as

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input

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blurs it just enough but not too much to

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keep global semantics and structural

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detail

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and denoise it to produce a new image

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that magically takes your edits into

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account

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and the model wasn't even trained with

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strokes or edits examples only with the

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original images

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of course in the case of a simple user

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edit

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they carefully designed the architecture

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to only generate the edited part and not

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recreate

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the whole picture this is super cool

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because it enables applications such as

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conditional image generation

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stroke based image synthesis and editing

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image and painting colorization and

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other inverse problems to be solved

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using a single unconditional modal

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without

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retraining it of course this will still

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work

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for only one generation style which will

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be the data set it was trained on

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however it's still a big advantage as

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you only need one data set

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instead of multiple related data sets

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with a GAN based

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image and painting network as we

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discussed the only downside

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may be the time needed to generate the

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new image as

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this iterative process takes much more

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time than a single pass

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through a more traditional gan based

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generative model

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still i'd rather wait a couple of

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seconds to have

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great results for an image than having a

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blurry fail

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in real time you can try it yourself

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with the code they made publicly

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available

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or use the demo on their website both

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are linked in the description

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let me know what you think of this model

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i'm excited to see what will happen with

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this

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sd based method in a couple of months or

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even less

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thank you for watching

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