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CommonCanvas: Open Diffusion Models Trained on Creative-Commons Images

Aaron Gokaslan · A. Feder Cooper · Jasmine Collins · Landan Seguin · Austin Jacobson · Mihir Patel · Jonathan Frankle · Cory Stephenson · Volodymyr Kuleshov

Arch 4A-E Poster #335
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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 [REDACTED].

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