  The infrastructure necessary for training state-of-the-art models is becoming
overly expensive, which makes training such models affordable only to large
corporations and institutions. Recent work proposes several methods for
training such models collaboratively, i.e., by pooling together hardware from
many independent parties and training a shared model over the Internet. In this
demonstration, we collaboratively trained a text-to-image transformer similar
to OpenAI DALL-E. We invited the viewers to join the ongoing training run,
showing them instructions on how to contribute using the available hardware. We
explained how to address the engineering challenges associated with such a
training run (slow communication, limited memory, uneven performance between
devices, and security concerns) and discussed how the viewers can set up
collaborative training runs themselves. Finally, we show that the resulting
model generates images of reasonable quality on a number of prompts.
