Traced the real provenance (Kevin's Codex dev sessions) and verified the
mechanism against the Krea 2 model on the render box. Two earlier claims
(07d2f0b) were wrong and are now fixed across the docs:
1. Mechanism: the 12 "deepstack chunks" are the 12 TEXT-encoder layer taps
Krea 2 conditions on (comfy/text_encoders/krea2.py: hidden_states
[2,5,8,...,35], (B,12,seq,2560) flattened to 30720), NOT the 3 vision
deepstack taps (qwen3vl.py deepstack_visual_indexes=[5,11,17]) the name
implies. 30720 % 12 == 0, so the node's split lands on the taps and the
per-layer gains do apply (no fallback) - confirmed from the model weights
(txtfusion.projector [1,12], layerwise blocks at 2560).
2. Provenance: the 12 values were NOT derived by a per-chunk sweep. They
were adopted from the third-party ComfyUI-ConditioningKrea2Rebalance node
(DEFAULT_WEIGHTS spike 8>10>7, matching STYLE_LAYER_PULL) plus the
shallow=structure/deep=appearance principle; only the scalar shape (0.45
->0.35) and cap (->0.9) were empirically tuned. The four role tables are
scalings of one template, so their agreement is design self-consistency,
not independent measurement - the "convergent evidence" framing is
withdrawn.
docs/deepstack-layers/ rewritten to separate verified mechanism, traced
provenance, and the still-unrun single-tap sweep. Corrections propagated to
the V9 technical paper (5.2 + architecture note + glossary), the V10 guide
and companion, custom_recipes/README, the V10 node doc, and CHANGELOG. Full
evidence chain preserved in local_records (gitignored).
New docs/deepstack-layers/ is the authoritative, reproducible account of
the 12 layers values, at three separated confidence levels:
1. Structure (verified from code): the values are gains on 12 equal
deepstack chunks - features from 12 successive Qwen3-VL encoder-depth
bands. Shown in kg_krea_v9/clip_hooks.py (the deepstack feature list)
and conditioning.py (the 12-way width split).
2. Semantics (convergent evidence): analyze_tables.py reads the live
tables and shows the four independently-tuned appearance tables
(style/palette/material/lighting) agree chunk-for-chunk - 0-4 carry
structure (monotonic depth ramp, all suppress), 5-6 transition, 7/8/10
carry appearance (all spike, chunk 8 strongest, then 10, then 7). Four
tables tuned for four different jobs converging is the measurement
encoded as data, not an assertion.
3. Precise response curve (fresh measurement): generate_sweep.py is a
turnkey single-chunk-spike sweep (the paper's section 16.4 method),
emitting each spike as a V10 custom recipe validated against the real
node code. Dry-run works anywhere; rendering needs the V10 nodes on a
ComfyUI box (single-chunk tables are not V9-expressible), so the
measured-verdict table is staged pending a V10-equipped render box.
SCORING.md defines the rubric.
Cross-linked from technical paper 5.2, the recipe guide, and
custom_recipes/README. Tooling lives under docs/ (excluded from registry
packs by .comfyignore). sweep_out/ is gitignored.
Kevin Gilper