Tanishq Mathew Abraham, Ph.D.(@iScienceLuvr):Abra: Scaling Diffusion Image Training A comprehensive scaling laws study of text-to-image diffusion models from Luma AI: "We present a systematic scaling law study for text-to-image diffusion models using Abra, a controlled family of flow-matching transformers trained across three orders of magnitude worth of compute (1019 to 1022 FLOPs), reaching significantly larger compute budgets than previous works. We demonstrate that diffusion models scale just as predictably as language models but require far more data to train optimally: compute optimality occurs at approximately 200 image tokens per parameter, ten times the Chinchilla compute-optimal prescription for LLMs." paper link:

Tanishq Mathew Abraham, Ph.D.@iScienceLuvr

2026.08.19 05:33

Abra: Scaling Diffusion Image Training A comprehensive scaling laws study of text-to-image diffusion models from Luma AI: "We present a systematic scaling law study for text-to-image diffusion models using Abra, a controlled family of flow-matching transformers trained across three orders of magnitude worth of compute (1019 to 1022 FLOPs), reaching significantly larger compute budgets than previous works. We demonstrate that diffusion models scale just as predictably as language models but require far more data to train optimally: compute optimality occurs at approximately 200 image tokens per parameter, ten times the Chinchilla compute-optimal prescription for LLMs." paper link: