CommonCanvas: An Open Diffusion Model Trained with Creative-Commons
  Images
Aaron Gokaslan A. Feder Cooper Jasmine Collins Landan Seguin Austin Jacobson Mihir Patel Jonathan Frankle Cory Stephenson Volodymyr Kuleshov
We assemble a dataset of Creative-Commons-licensed (CC) images, which we use
to train a set of open diffusion models that are qualitatively competitive with
Stable Diffusion 2 (SD2). This task presents two challenges: (1)
high-resolution CC images lack the captions necessary to train text-to-image
generative models; (2) CC images are relatively scarce. In turn, to address
these challenges, we use an intuitive transfer learning technique to produce a
set of high-quality synthetic captions paired with curated CC images. We then
develop a data- and compute-efficient training recipe that requires as little
as 3% of the LAION-2B data needed to train existing SD2 models, but obtains
comparable quality. These results indicate that we have a sufficient number of
CC images (~70 million) for training high-quality models. Our training recipe
also implements a variety of optimizations that achieve ~3X training speed-ups,
enabling rapid model iteration. We leverage this recipe to train several
high-quality text-to-image models, which we dub the CommonCanvas family. Our
largest model achieves comparable performance to SD2 on a human evaluation,
despite being trained on our CC dataset that is significantly smaller than
LAION and using synthetic captions for training. We release our models, data,
and code at
https://github.com/mosaicml/diffusion/blob/main/assets/common-canvas.md