Conditional Diffusion on Web-Scale Image Pairs leads to Diverse Image Variations

Understand

Generating image variations, where a model produces variations of an input image while preserving the semantic context has gained increasing attention.

• Current image variation techniques involve adapting a text-to-image model to reconstruct an input image conditioned on the same image.
• We first demonstrate that a diffusion model trained to reconstruct an input image from frozen embeddings, can reconstruct the image with minor variations.
• Second, inspired by how text-to-image models learn from web-scale text-image pairs, we explore a new pretraining strategy to generate image variations using a large collection of image pairs.