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

In an exploration of rapid and cost-effective training for text-to-image diffusion models, the authors conducted a 24-hour speedrun combining various architectural and training optimizations previously explored in their series. Utilizing 32 H200 GPUs with a compute budget of $1,500, the experiment aimed to showcase advancements in the field, demonstrating significant progress from earlier expensive training phases. The approach integrated pixel-space training, efficient token routing, perceptual losses, and representation alignment techniques to enhance model performance. Despite some remaining issues, such as texture glitches and limited data diversity, the model's performance in terms of prompt following and visual consistency was promising. The experiment highlights how modern engineering practices can produce meaningful results within a constrained timeframe and budget. The authors open-sourced their code to allow for community replication and iteration, aiming to inspire further exploration and refinement in diffusion model training.

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