Extracting Training Data from Diffusion Models

Understand

Image diffusion models such as DALL-E 2, Imagen, and Stable Diffusion have attracted significant attention due to their ability to generate high-quality synthetic images.

• In this work, we show that diffusion models memorize individual images from their training data and emit them at generation time.
• With a generate-and-filter pipeline, we extract over a thousand training examples from state-of-the-art models, ranging from photographs of individual people to trademarked company logos.
• We also train hundreds of diffusion models in various settings to analyze how different modeling and data decisions affect privacy.