Replaces "borrow a template + eyeball renders" with a staged pipeline for
setting the 12 layer-gain values, cheapest/most-targeted first:
- Stage 0 (done, local): extract_model_prior.py reads Krea 2's own learned
per-tap weighting (txtfusion.projector [1,12]) from the checkpoint;
compare_to_model_prior.py scores each shipped table against it. Finding:
the model leans hardest on taps 7>9>4>10>8 but the tables spike 8>10>7
(rank-corr only +0.58..+0.64) - the borrowed template does not track how
the model uses the taps. model_prior.json records the values.
- Stage 1 (harness ready, no render): probe_node/ is a tiny ComfyUI node
that saves per-tap conditioning signatures; probe_selectivity.py does a
variance decomposition (eta^2) of each tap against palette/structure/
texture/lighting over a controlled reference set; generate_probe_graphs.py
builds the encode-only graphs. The selectivity math passes a synthetic
self-test (recovers an injected tap->attribute map exactly). Running it for
real needs V10 + the probe node installed on a box, plus controlled image
sets - it renders nothing (text-encoder passes only).
- Stages 2-3 (documented, need a V10 box): metric-scored render validation,
then black-box optimization of the 12-gain vector against a stated
objective, held-out validated.
README section 4 rewritten as the methodology. All scripts run/validate
locally; tooling stays under docs/ (excluded from registry packs).
