Blue Mage: Tracing Learning Dynamics of Web-Scale Text-to-Image Flow Models

We introduce Blue Mage, an open training record for web-scale text-to-image flow pretraining, in which every saved generator is bound to the updates that produced it. Pythia and OLMo made language-model pretraining a shared experimental object; text-to-image pretraining still reaches the public as its endpoint, rarely as the states that led to it. Beyond a data order, a flow-matching record has to fix what each batch slot draws: a sample, a flow time, a noise draw, and a caption decision. With the corpus distributed as image bytes, Blue Mage fixes all four in a closed-form plan before training, so the batch behind any update and the exposure of any concept before any saved state are recomputed from the plan; it repeats curated canary images against loss-masked twins and keeps resumable training states. We train MMDiT of 0.5B, 2.0B, and 3.9B parameters from scratch on nested corpora of 1M, 10M, and 100M images from the GPIC dataset. Following each run through its saved states, we ask when an attribute-entity pair is learned and when a repeated image becomes retrievable. Exposure sets how well a pair is rendered; training progress sets when. A familiar entity becomes easier to render and harder to control. Incorrect bindings rise with correct ones, peak with their exposure, and recede. Retrieval takes more updates yet fewer repetitions in a larger corpus at the highest dose, and varied captions weaken retrieval through the trained caption while strengthening it through captions never trained on. We will release the code, plans, exposure records, and saved states, and hope they serve open research on generative models throughout their training.