Training a 210M text-to-image DiT from scratch on one GPU: what I measured [P]

Excerpt

I trained a 210M-parameter text-to-image diffusion transformer from scratch (3.5 days, one RTX PRO 6000, 4.2M images at 256²) mainly to understand the recipe end to end. Three measurements came out of it that I have not seen stated plainly elsewhere, so I'm posting those rather than the samples. 1. Learned null attention slots become the sink. Following the register-token idea, the model has 16 register tokens in the image stream plus 2 learned key/value slots appended to every cross-attention.