How Does AI Work as a Creative Tool?

Any artist will have a particular style, sometimes inspired by someone else. For example, during the American occupation of Japan after WWII, the style of Disney's big eyed cartoon animals (think “Bambi”) were adopted by Japanese artists to modify their own cartoon “base”. That is how the modern form of manga comics emerged. AI can also copy a style, and transfer it to a different base. You can train AI, for example, on van Gough's painting style, and apply it to a picture of your grandmother. Since this process works like the neurons in your brain, it is called “Neural Style Transfer” (NST).

In the case of African textile artists, new styles can be a difficult sell. Their buyers are usually focused on tradition. You can go to Europe for the hot new fashion, but international buyers tend to want kente weavers to keep making kente. Could NST offer a way to create new markets for traditional African fabricators?

In the software that follows, you will be able to experiment with AI that takes African fabric styles and merges them with a base using an NST. The African textile makers we work with have OK'ed this activity for educational purposes, and in turn we helped them set up an online store: africanfuturist.org. That's the “keep it real” part of this activity: making sure the value gets returned to the communities who did the original work. Ready to help out?

First you will need to pick the “style” (sort of like painting with a texture) that the NST learns from. Then you will need to pick the “base” that it modifies with the style.

Experiment with this a bit. Was the African cloth the base or the style? What happens when you switch it up?

Adire Cloth	Kente Cloth	Mud Cloth	Adinkra Cloth	Kuba Cloth

Here are some base image samples. You can also upload an image of your own choosing.

The Statue of Liberty	Frederick Douglass	Rhianna	The Mona Lisa	Mae Jemison

So how does this work? What we are doing is called style transfer in which the style and coloring of one image is transferred to the presentation of another image to create a unique rendition of the two images together.

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