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One training step
Hide a real training picture under noise. Ask the model what noise was added. Score how close it got. That is the whole loop — running here, live, on the real model.
Pick a training picture
The step—
—
The score
answer implies
Training would now nudge the weights to shrink that error, and repeat 30,000 times. This tab cannot — these weights are already finished.
Thirty thousand steps
Ten prompts, drawn by the real model at 27 points in its own run. Not a simulation — this is what it could actually do at each of them.
Drag the slider to move through the run.
Why the curve lies
The loss, with how much the pictures actually changed drawn over it. They agree closely — which is the trap. Both fall 84% of the way by step 200, while the output is still coloured smears, and both look flat by step 1,000 with a factor of seven still to go. That last 7× is the difference between a blob and a monster.
Starting…