Kevin Gilper
839fa6e246
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).
4 lines
148 B
Python
4 lines
148 B
Python
from .kg_conditioning_probe import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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