Dreaming up imaginary landscapes with Runway ML & StyleGAN
A no-code and step-by-step account of training a Generative Adversarial Network (GAN) to generate places that don’t exist
Both Nadia Domide’s and my (yes we’re both called Nadia haha) day-to-day work is focussed on Artificial Intelligence and Design. Intrigued by creative AI practices, we decided to not leave this excitement to client requests and to start experimenting.
In this article, we’ll walk you through the entire process of how we created our first generative AI visuals using Runway ML.
Runway: ML for Artists
Runway ML is a free software making Machine Learning accessible to artists and creatives. They offer the option to (re-)train your own models such as StyleGAN which we were most eager to try out. Around the time we were having these talks, Runway ML put out an open call for their residency program and we decided to apply. While we weren’t chosen — by the time we finished writing our application, we were so excited we decided to do it anyway.
These places do not exist(.com)
After some brainstorming and discussions, different streams of inspiration started colliding. We were really inspired by the parody projects on thispersondoesnotexist.com, such as thisartworkdoesnotexist.com & thisworddoesnotexist.com — so we asked ourselves: Can we create places that don’t exist? Can AI dream up real presentations of unreal places? Can we generate imaginary landscapes? Long story short: yes, we can.
The complete step-by-step process of generating visuals by (re-)training a StyleGAN in Runway ML
1. Data collection
One of the important parts of an ML project is collecting the training data. We started curating a dataset of approximately 3000 images from Google Earth View. These images were displayed on the world’s largest billboard to bring a bit of zen to New York’s hectic Times Square during the holidays. They are both stunning and a well-curated (AKA lazy) dataset to obtain.
2. Picking the right pre-trained model to start with
Runway ML currently offers an easy way to do image synthesis, by using StyleGAN to generate photorealistic images. We chose to start with their available pre-trained model, called Landscapes (see image below).
By doing transfer learning on the StyleGAN model it allowed us to train our algorithm in a shorter time. We preprocessed the images to squared and centered ones, opted for 3000 training steps, and hit the exciting purple ‘Start Training’ button.
3. Training on our dataset
We trained the model for a couple of hours. During this training time, we could observe how the FID (Frechet Inception Distance) score was changing (see image below). This metric simply computes how similar a generated image is from the real ones in the dataset. Having a low value is an indicator that the real data and the generated one have similar characteristics.
4. Generating outputs from the latent space
Ta-da! Once the training was completed, we could use it to generate new images and videos using random points in the latent space.
What is a latent space? It is a magical multi-dimensional hidden space with no meaning, filled with points. The beauty of this space is that the generative model learns to map these points to output images. And a space walk is simply a series of images that show a transition between two or more generated images.
Runway gives you the option to export images and a video of a so-called latent space walk.
Join us in our collective imagination
We’ve generated 120 of these images. Astonished by their beauty and our brains seeking to think up stories, we questioned what we should do with these fictional places. Might these landscapes have value when put in the hands of people? How can you create memories of places you have never been to (and never will)?
If you like the idea of generating imaginary landscapes & assigning meaning to places that don’t exist, please join us in this small experiment of collective imagination.
We’ll start with a shot of our fictional historical landmarks — the Dustry Blue meets Sandy View and the Coral Candy Pickle Grove place — pictured in the images below.
Big thanks to Runway ML, and everyone that has contributed to the open-source files that made this experiment possible. Now go out to craft your own StyleGAN models and imaginaries. Thank you for reading with us!
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