PRX Part 3 — Training a Text-to-Image Model in 24h!

Back to Articles PRX Part 3 — Training a Text-to-Image Model in 24h! Team Article Published March 3, 2026 Upvote 1 David Bertoin Bertoin Follow Photoroom Roman Frigg photoroman Follow Photoroom Jon Almazán jon-almazan Follow Photoroom Introduction Welcome back 👋 In the last two posts (Part 1 and Part 2), we explored a wide range of architectural and training tricks for diffusion models. We tried to evaluate each idea in isolation, measuring throughput, convergence speed, and final image quality, and tried to understand what actually moves the needle. In this post, we want to answer a much more practical question: What happens when we combine all the tricks that worked? Instead of optimizing one dimension at a time, we’ll stack the most promising ingredients together and see how far we can push performance under a strict compute budget. To make things concrete, we’re doing a 24-hour speedrun: 32 H200 ~$1500 total compute budget (2$/hour/GPU) This is very far from the early diffusion days, where training competitive models could cost millions of dollars. The goal here is to demonstrate how much the field has evolved and how far careful engineering can take you in just a single day of training. This speedrun is not just a fun experiment. It will likely serve as the foundation for our large-scale training recipe going forward. Alongside the results, we’re also open-sourcing our code (Github), which contains: The training code used for this speedrun The experimental framework f...