Region lock: a keyframe pin can cover part of a frame. Transparent areas of an
i2v anchor are dropped from the packed sequence entirely — not blanked, not
noised, absent — so the model invents them. Row deletion re-derives the layout
from upstream's own output; two patches share one keep-mask so they cannot
disagree. Surfaced through the anchor's alpha, which needs no new wire and works
in the Editor. Excluded pixels are filled with the kept area's mean before the
VAE so no hard edge bleeds in. EXPERIMENTAL: fl2va saw whole condition frames.
Taste bias: the liked direction Studio already learns is now also added AFTER
H3's token refiner, where it lands as sent instead of being mixed across tokens
and normalized away. Relative to the span's own row norm, so one strength means
the same thing on every prompt. How far to push is a fifth learned gain,
centred on 0 and signed; h3_taste_bias is the manual override. Needs 3 liked
runs.
Gain state generalized to per-key centres, bounds and exploration width.
Tests: 1608 passed (+46). Untested on a rental.
The gains read only the scalar reward, so "Missing concept" — reward +0.10
because quality was fine — nudged every gain slightly TOWARD the run that
missed the prompt, and taught the audio gain something about a complaint
that had nothing to do with audio.
Every rating already carries three per-axis signals in [-1, +1]. Route
them: concept -> prompt + prompt_scale, detail/quality -> video, and audio
keeps the overall reward because no rated axis covers it.
The combiner is min, not mean. These axes are conjunctive — the video gain
has to satisfy detail AND quality — so a satisfied axis must not mask a
failed one. Averaging let it: "Missing details" came out at -0.08 for the
gain that governs detail. It is -1.00 now.
Recovering four parameters from one scalar worked, but only by waiting for
random perturbations to decorrelate. Each rating now teaches the parameter
it names.
The four H3 render strengths shipped as hand-set dials. They should not
have: four scalars is a SMALLER search than the sigma profile, which
already converges on ratings alone, so typing them is the user doing work
the loop can do.
Same ES update as the sigma profile, with one difference — that profile is
centred on 0, so its applied value IS its perturbation; these are centred
on 1.0, so the perturbation is (applied - value) and that is what the
reward is credited to. Exploration narrows on a like, widens otherwise,
and is seeded from the run seed so a repeat repeats.
Learned values reach the sampler through the conditioning meta, the bridge
h3 token weighting already uses: Studio owns the key, the sampler owns the
application. h3_gain_mode defaults to "learned" and ignores the widgets;
"manual" uses them as typed and still works with no Refiner in the graph.
Learning runs on every rating regardless of the toggle, the same rule
value_guidance follows.
Also fixes four note_on_change calls added earlier this session that were
missing the required tag argument — they would have raised on the non-H3
and unreadable-tags paths.
digital-garbage