Three measurements from training a 210M text-to-image DiT from scratch on one GPU

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An r/MachineLearning post (106 upvotes) reports a 210M-parameter diffusion transformer trained in 3.5 days on one RTX PRO 6000 over 4.2M images at 256², and publishes the diagnostics rather than the samples. Two learned key/value slots appended to cross-attention absorb ~90% of attention mass at mid-noise while the EOS token, the usual sink, drops to ~4%, and register vectors grow to 4-13x the norm of image tokens. Flow-matching loss moved only 0.805 to 0.754 while held-out FID went 33.7 to 27.0 and FD-DINOv2 570 to 218, so the loss is a health signal, not a quality signal. Training-time timestep shift of 2.8 at 20 steps (FID 27.0) beat no-shift at 20 steps (27.3) and nearly matched 50 steps (26.6).