REFRESH#4 Poster

Uncanny images generated from keywords used as graphical elements for festival poster

Cover Image for REFRESH#4 Poster

For the 2021 edition of REFRESH Festival in Zurich the graphic designer Patrik Ferrarelli teamed up with interaction designer Andreas Refsgaard and a machine-learning algorithm. Using CLIP, a machine learning model trained by OpenAI to determine which text best fit a given image, in combination with generative machine learning models, Andreas generated a series of uncanny images around the keywords of REFRESH. Graphic designer Patrik Ferrarelli used these images as graphical elements in this year's posters and visuals for the web.

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The text prompts came from this year's keywords for the REFRESH festival and included simple text snippets like «Immersive journalism», «Immersive Play», «Digital identities» or «Techno-biological futures». From a technical perspective, Andreas used a combination of techniques initially developed by Ryan Murdoch and Katherine Crowson.

Simply put, CLIP guides a generator (like VQGAN, BigGAN or StyleGAN) to generate an image that corresponds to a given text:

«CLIP is a model that was originally intended for doing things like searching for the best match to a description like «a dog playing the violin» among a number of images. By pairing a network that can produce images (a «generator» of some sort) with CLIP, it is possible to tweak the generator’s input to try to match a description.» Ryan Murdoch

Although perhaps more uncanny than realistic, the outputs ended up suiting this year's REFRESH keywords.

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Want to try it out for yourself?

Below are the Google Colab notebooks used for this project:

Latent3Visions: CLIP+Taming by Ryan Murdoch

CLIP Guided Diffusion HQ by Katherine Crowson

Zoetrope 5.5 by Bearsharktopusdev

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REFRESH #4, Zurich 2021