A functional text-to-image model trained from scratch for $21K using a pixel-space architecture (P-JiT) that represents a 512² image in the token count others use for 256² — a 4× smaller attention context window and 11× faster convergence than their prior DiT baseline, with Apache 2.0 weights. If the token-reduction claims hold, this points to dramatically cheaper diffusion inference and training, meaning you could self-host or fine-tune a capable image model on a modest budget rather than paying API rates — though it's an experimental release and a stepping stone, so expect quality and control gaps versus FLUX-class models.