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