h3_block_repeat runs the named block(s) on their own output before
passing it on — a second pass that never leaves latent space. To first
order this doubles that block's contribution; the nonlinear part is a
genuine second refinement of its own result. ~2% of a step per repeat.
detail_probe scores whether that (or anything else) actually sharpened
the picture, by comparing a run against the previous one on three
numbers:
detail high-frequency energy — did fine structure increase
structure low-frequency survival — same shot, or a different one
edge-aligned did the EXTRA detail land on existing edges, or spread
The third is the one that earns its place: noise is high-frequency too,
so `detail` alone cannot tell sharpening from grain — and grain is
exactly what an out-of-distribution stream would produce. Verified on
video-latent-shaped data: unsharp-mask reads detail x3.04 / edge +0.885
("sharper"), uniform noise reads detail x1.12 / edge -0.248 ("grain").
Both raised detail; only edge-alignment separated them.
Measured in latent space deliberately — the tensor is already in hand at
the call site DynaShift banks from, so no VAE decode and no new
plumbing. A real approximation, valid for the A/B it is for (same seed,
identical decoder both sides) and documented as ratio-only.
Reuses block_influence's collection switch rather than adding a second
research toggle, and pairs CONSECUTIVE runs, which is the workflow
anyway: generate with it off, generate with it on, read the row.
Also scores second_pass sharpen, quality_sharpness and segmented
detailing — every mechanism here that claims detail and is currently
judged by eye.
digital-garbage
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