This content originally appeared on DEV Community and was authored by AI News
TL;DR: Apple dusted off an old AI trick—Normalizing Flows—and spiced it up with Transformers to create two new image generators: TarFlow and STARFlow. Unlike diffusion or token-based autoregressive models, flows learn a reversible “noise image” mapping that gives exact likelihoods. TarFlow chops images into patches and predicts pixel values in sequence (no token compression!), while STARFlow works in a smaller latent space before upsampling to high-res, and even plugs in lightweight language models for text prompts.
The big sell? These flow-based models could run on your device, offering crisp detail and probability-aware outputs without constant cloud crunching. It’s a different path than OpenAI’s GPT-4o, which treats images like giant token streams in the cloud—flexible but heavy and potentially slower—whereas Apple’s approach is built for speed and efficiency in our pockets.
This content originally appeared on DEV Community and was authored by AI News