Training Design for Text-to-Image Models: Lessons from Ablations

Back to Articles Training Design for Text-to-Image Models: Lessons from Ablations Team Article Published February 3, 2026 Upvote 57 +51 David Bertoin Bertoin Follow Photoroom Roman Frigg photoroman Follow Photoroom Jon Almazán jon-almazan Follow Photoroom Welcome back! This is the second part of our series on training efficient text-to-image models from scratch. In the first post of this series, we introduced our goal: training a competitive text-to-image foundation model entirely from scratch, in the open, and at scale. We focused primarily on architectural choices and motivated the core design decisions behind our model PRX. We also released an early, small (1.2B parameters) version of the model as a preview of what we are building (go try it if you haven't already 😉). In this post, we shift our focus from architecture to training. The goal is to document what actually moved the needle for us when trying to make models train faster, converge more reliably, and learn better representations. The field is moving quickly and the list of “training tricks” keeps growing, so rather than attempting an exhaustive survey, we structured this as an experimental logbook: we reproduce (or adapt) a set of recent ideas, implement them in a consistent setup, and report how they affect optimization and convergence in practice. Finally, we do not only report these techniques in isolation; we also explore which ones remain useful when combined. In the next post, we will publish the full tra...