Kevin Gilper
07d2f0b763
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.
49 lines
2.1 KiB
Markdown
49 lines
2.1 KiB
Markdown
# Scoring the deepstack sweep
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After `generate_sweep.py --server ...` renders the grid, score every spike
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render **against the control** (`<ref>__sweep-control.png`) to determine what
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each chunk carries. The control and the spike share prompt, seed, reference,
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strength, pooled channel, and all other chunks - so any visible difference is
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attributable to the spiked chunk alone.
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## Procedure
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For each reference (`subject`, `palette`) and each chunk `L`:
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1. Open `<ref>__sweep-control.png` and `<ref>__sweep-chunk-LL-gain-4.png`
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side by side.
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2. Judge which single aspect changed **most** relative to the control, and how
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strongly (none / subtle / clear / dominant):
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| Aspect | What to look for |
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| --- | --- |
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| structure | subject shape, pose, layout, spatial composition shifting toward the reference |
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| palette | overall color cast, hue relationships moving toward the reference |
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| texture | surface/material grain, finish, micro-detail |
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| lighting | light direction, contrast, glow, shadow, mood |
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| (none) | no meaningful change from control |
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3. Record: `chunk L -> <aspect> (<strength>)`. If two aspects move together,
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record both with the dominant one first.
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## Recording
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Fill the verdict table in [README.md](README.md) - one row per chunk, with the
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verdict from each reference and a consensus column. A determination is
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"solidified" when:
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- both references agree on each chunk's dominant aspect, and
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- the measured structure->transition->appearance ordering matches the
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convergent-table prediction (chunks 0-4 structure, 5-6 transition, 7/8/10
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appearance with 8 strongest), OR any disagreement is documented with its
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consequence for the tables.
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If a scoring pass is done by an agent, have a second independent agent score
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the same grid blind and reconcile; note both in the record.
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## Optional: multi-level confirmation
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Re-run with `--gains 2 4 6` to confirm each chunk's response is monotonic in
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gain (a real carrier gets stronger with a bigger spike; noise does not). Note
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the gain at which each chunk's effect first becomes clearly visible.
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