Researchers from MIT and Google have collaborated to develop StableRep technology, which trains detailed and efficient AI image models using AI-generated images. StableRep employs multi-positive contrastive learning methods and trains on millions of labeled synthetic images, achieving significant results in ImageNet classification. Despite its success, StableRep's image generation is relatively slow, suffers from semantic mismatch issues, and requires the underlying model to be initially trained on real data. The technology has been open-sourced on GitHub.