Practical Artificial Intelligence for Stage Design

Artificial intelligence (AI), machine learning, and generative art conjure up images ranging from mesmerizing to downright terrifying. Our popular culture is suffused with adversarial depictions of robots or AI draining all happiness, creativity, and life from humanity.

Since the early 2010s, however, the landscape of AI usage has changed dramatically. The proliferation of easy-to-use generative models–AIs trained on data to create new data–has led to a surge of recognition on social media. From the 2014 “puppyslugs” of Google’s DeepDream to hyper-realistic deepfakes and award-winning artworks, generative AI has firmly stepped onto the world stage.

In theatre, we’re used to new technology being our swift downfall. Here’s the good news: you are not one robot away from replacement, at least with current technology. AI is more comparable to the workflow enhancements and new possibilities that computer-assisted drawing or electric lighting offered theatre. So, just what are these tools that creatives find in their hands, and how might they apply to theatre?

As a projection designer by trade, I was initially intrigued by AI due to its possibilities to create generative video, rapidly respond to new prompts, and perform creativity. As an emerging designer, most of my work has been as an animator on productions. This usually means implementing a designer’s vision and storyboards by parsing libraries of stock footage and creating new visual effects. AI has helped me find shortcuts in the content-creation pipeline by allowing me to create almost fully realized images that fit our exact stylistic needs. I want to model some of the approaches I’ve used for other designers. Let’s start by breaking down one way we can use a small subset of AI-accelerated tools: text-to-image models.

These generative models can complement and expand your toolkit as a designer or theatremaker.

You might have used a program like Photoshop or Vectorworks to create a line before. The commands are simple and direct. Photoshop doesn’t guess what kind of line you want. You prescribe everything about that line: where to start and stop, the size of the line, its color.

The premise of text-to-image AI is that you can now use simple, natural language to describe what you want, and a machine “model” trained to recognize that language makes its best guess at that image. What you lose in specificity and control, you gain in the ability to rapidly iterate almost fully realized images. The machine takes many different steps to do this, engaging in a complex process that, in the case of image generation, pits machines against each other in a contest of generating and spotting machine images.

These generative models can complement and expand your toolkit as a designer or theatremaker. To illustrate these benefits, I’ll show examples from a few simple tools that have various degrees of adherence to open access practices: the text-to-image models available through Midjourney and DALL-E 2. Midjourney is currently in an open beta with free initial generations and a paid subscription system and DALL-E 2 is also in an open beta, with free initial generations and a pay-as-you-use credits system. Numerous open-source implementations and new models are released frequently. For example, Stable Diffusion, an open-source text-to-image model, was just released in August. Craiyon is a free alternative for early explorations.

An AI Toolkit

For the purposes of exploring these tools, I’ll be applying these text-to-image models in a prospective design for A Midsummer Night’s Dream by William Shakespeare. Here’s the gist: lots of hijinks about royals getting married. Then, fairies interfere and cause even more hijinks. It is a classic comedy of mistaken identities, trickery, and mischief often set in idyllic woodlands. Let’s take a look at the play from the perspective of a set designer in the research stage.

The first use of AI may seem most obvious and, for many, the most “acceptable”: generating concepts, mood images, and tone research. For many designers, the initial part of a scenic design begins confronting an array of Google Images, Pinterest, and their own collected visual research. Let’s see how prompting an AI works.

I asked the models to generate the following: The mysterious Fairyland, whose moon glimmers and dewdrops rest on the forested grasses.