StyleCLIPDraw: Text-to-Drawing Synthesis with Artistic Control

Have you ever dreamed of taking the style of a picture, like this cool TikTok drawing style on the left, and applying it to a new picture of your choice? Well, I did, and it has never been easier to do. In fact, you can even achieve that from only text and can try it right now with this new method and their Google Colab notebook available for everyone (see references).

Simply take a picture of the style you want to copy, enter the text you want to generate, and this algorithm will generate a new picture out of it! Just look back at the results above, such a big step forward! The results are extremely impressive, especially if you consider that they were made from a single line of text! If that sounds interesting, watch the video and learn more!

►Read the full article: https://www.louisbouchard.ai/clipdraw/
►CLIPDraw: Frans, K., Soros, L.B. and Witkowski, O., 2021. CLIPDraw:
exploring text-to-drawing synthesis through language-image encoders. https://arxiv.org/abs/2106.14843
►StyleCLIPDraw: Schaldenbrand, P., Liu, Z. and Oh, J., 2021.
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis. https://arxiv.org/abs/2111.03133
►CLIPDraw Colab notebook: https://colab.research.google.com/github/kvfrans/clipdraw/blob/main/clipdraw.ipynb
►StyleCLIPDraw code: https://github.com/pschaldenbrand/StyleCLIPDraw
►StyleCLIPDraw Colab notebook: https://colab.research.google.com/github/pschaldenbrand/StyleCLIPDraw/blob/master/Style_ClipDraw_1_0_Refactored.ipynb
►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 taking a

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picture like this cool tick tock drawing

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style and applying it to a new picture

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of your choice well i did and it has

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never been easier to do in fact you can

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even achieve that from only text and you

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can try it right now with this new

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method and their google collab notebook

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available for everyone simply take a

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picture of the style you want to copy

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enter the text you want to generate and

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this algorithm will generate a new

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picture out of it look at that such a

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big step forward the results are

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extremely impressive especially if you

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consider that they were made from a

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single line of text here i tried

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imitating the same style with another

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text input to be honest sometimes it may

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look a bit all over the place especially

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if you select a more complicated or

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messy drawing style like this one

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speaking of something messy if you are

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like me and your model versioning and

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resource tracking looks like this you

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may be the perfect candidate to try the

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sponsor of two days video which is none

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other than weights and biases i always

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assumed i could stack folders like this

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and simply add old v1 v2 v3 and so on to

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my file names without any problem until

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i had to work with someone while it may

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be easy for me to find my old tests it

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was impossible to explain my thought

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process behind this mess and was my

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teammate's nightmare if you care about

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your teammates and reproducibility don't

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do like i did and give weights and

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biases a shot no more notebooks or

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results saved everywhere as it creates a

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super friendly user dashboard for you

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and your team to track your experiments

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and it's super easy to set up and use

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it's the first link in the description

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and i promise within a month you will be

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completely dependent

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as we said this new model by peter

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schaldenbrunn ethel called style clip

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draw which is an improvement upon clip

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draw by kevin franz at all takes an

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image and takes as inputs and can

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generate a new image based on your text

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and following the style in the image so

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the model has to both understand what's

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in the text and the image to correctly

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copy its style as you may suspect this

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is incredibly challenging but we are

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fortunate enough to have a lot of

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researchers working on so many different

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challenges like trying to link text with

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images which is what clip can do quickly

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clip is a model developed by openai that

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can basically associate a line of text

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with an image both the text and images

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will be encoded similarly so that they

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will be very close to each other in the

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new space they are encoded in if they

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both mean the same thing using clip the

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researchers could understand the text

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from the user input and generate an

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image out of it if you are not familiar

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with clip yet i would recommend watching

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a video i made about it together with

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dolly earlier this year but then how did

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they apply a new style to it clip is

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just linking existing images to texts it

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cannot create a new image indeed we also

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need something else to capture the style

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of the image sent in both the textures

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and shapes well the image generation

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process is quite unique it won't simply

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generate an image right away rather it

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will draw on a canvas and get better and

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better over time it will just draw

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random lines at first and create an

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initial image this new image is then

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sent back to the algorithm and compared

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with both the style image and the text

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which will generate another version this

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is one iteration at each iteration we

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draw random curves again oriented by the

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two losses we'll see in a second this

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random process is quite cool since it

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will allow each new test to look

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different so using the same image and

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same text as inputs you will end up with

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different results that may look even

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better here you can see a very important

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step called image augmentation it will

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basically create multiple variations of

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the image and allow the model to

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converge on results that look right to

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humans and not simply on the right

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numerical values for the machine this

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simple process is repeated until we are

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satisfied with the results so this whole

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model learns on the fly over many

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iterations optimizing two losses we see

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here one for aligning the content of the

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image with the text sent and the other

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further style here you can see the first

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lust is based on how close the clip

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encodings are as we said earlier where

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clip is basically judging the results

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and its decision will orient the next

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generation the second one is also very

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simple we send both images into a

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pre-trained convolutional neural network

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like vgg which will encode the images

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similarly to clip we then compare these

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encodings to measure how close they are

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to each other this will be our second

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judge that will orient the next

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generation as well this way using both

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judges we can get closer to the text and

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the wanted style at the same time in the

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next generation if you are not familiar

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with convolutional neural networks and

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encodings i will strongly recommend

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watching the video i made explaining

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them in simple terms this iterative

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process makes the model a bit slow to

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generate a beautiful image but after a

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few hundred iterations or in other words

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after a few minutes you have your new

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image and i promise it's worth the wait

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it also means that it doesn't require

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any other training which is pretty cool

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now the interesting part you've been

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waiting for indeed you can use it right

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now for free or at least pretty cheaply

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using the collab notebook linked in the

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description below i had some problems

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running it and i would recommend buying

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the pro version of collab if you'd like

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to play with it without any issues

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otherwise feel free to ask me any

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questions in the comments if you

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encounter any problems i pretty much

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went through all of them myself to use

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it you simply run all cells like that

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and that's it you can now enter a new

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text for the generation or send a new

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image for the style from a link and

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voila now tweak the parameters and see

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what you can do if you play with it

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please send me the results on twitter

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and tag me i'd love to see them as they

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state in the paper the results will have

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the same biases as the models they use

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such as clip which you should consider

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if you play with it of course this was a

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simple overview of the paper and i

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strongly invite you to read both clip

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draw and style clip draw for more

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technical details and try their collab

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notebook both are linked in the

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description below thank you once again

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weights and biases for sponsoring this

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video and huge thanks to you for

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watching until the end i hope you

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enjoyed this week's video let me know

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what you think and how you will use this

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new model

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