dense-plantain probe results README

README.md

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+---
+language: en
+license: apache-2.0
+base_model: black-forest-labs/FLUX.2-klein-base-4B
+library_name: diffusers
+tags:
+  - interpretability
+  - per-head-attention
+  - paired-prompt-probe
+  - human-pose
+  - flux2
+  - vision-banana
+  - arxiv:2604.20329
+pipeline_tag: image-to-image
+---
+
+# dense-plantain
+
+A per-head attention probe of FLUX.2 Klein 4B testing whether the base model represents canonical body pose as a separable axis from expressive pose deformation, parallel to the canonical-vs-deformed coordinate distinction in dense human-pose models (DensePose, SMPL).
+
+## Thesis
+
+Dense human-pose representations canonically encode body shape in a fixed reference pose (T-pose) plus a deformation that maps to the actual pose. Whether image-generation models develop an analogous axis without explicit supervision is the question. dense-plantain pairs descriptions of a body subject in canonical T-pose against the same body subject in an expressive pose, and tests whether per-head attention shifts systematically.
+
+## Method
+
+Twenty-five paired prompts holding body subject and setting constant; only pose configuration varies (canonical T-pose vs. expressive pose). Per-head capture protocol identical to the rest of the plantain probe family.
+
+Rigor add-ons: per-head Cohen's d effect size; split-half consistency via 100 random 50/50 stimulus splits.
+
+## Results
+
+| Metric                         | Value           | Significance              |
+|--------------------------------|-----------------|---------------------------|
+| Heads with \|t\| > 3           | 8,263 (50.6%)   | 6.2× empirical null p99   |
+| Heads with \|t\| > 5           | 4,614 (28.3%)   | 659× empirical null p99   |
+| Heads with \|d\| > 0.8 (large) | 6,164 (37.8%)   | —                         |
+| Split-half r (median)          | 0.863           | [0.85, 0.87] IQR          |
+| Max \|t\|                      | 22.45           | —                         |
+
+**Top blocks by max \|t\|:**
+- single[0]: max\|t\|=22.45, 283/768 heads at \|t\|>3, median \|d\|=0.45
+- single[3]: max\|t\|=22.06, 389/768 heads at \|t\|>3, median \|d\|=0.61
+- single[9]: max\|t\|=21.66, 232/768 heads at \|t\|>3, median \|d\|=0.39
+- joint[3]: max\|t\|=20.18, 138/192 heads at \|t\|>3, median \|d\|=1.02
+- joint[4]: max\|t\|=19.62, 131/192 heads at \|t\|>3, median \|d\|=1.01
+
+**Interpretation.** The body-pose axis is strong (659× null at |t|>5) and highly reproducible (split-half r=0.86). Over half of all 16,320 attention heads register the canonical-vs-expressive pose distinction at |t|>3, and over a third reach Cohen's d > 0.8. Localization is mixed: max signal is in early single blocks (single[0]/[3]/[9]), but joint-block median |d| is highest (≥1.0 in joint[3] and joint[4]), suggesting pose information is initially routed through cross-attention text-image fusion (joint blocks) and then maintained through early single-block transformer processing.
+
+The result places Klein among the models that have an internal canonical-pose representation without explicit pose supervision, parallel to but more diffuse than the structured axis encoded in DensePose-trained networks.
+
+## Status
+
+Probe complete. No LoRA training; this is a base-model interpretability finding.
+
+## Limitations
+
+The T-pose vs. expressive-pose pair has unmatched articulation complexity — expressive poses involve more anatomical regions deviating from canonical position than T-pose does. A residual contributor to the signal could be "amount of articulation" rather than canonical-vs-deformed status specifically. A follow-up controlling articulation (e.g., A-pose vs. expressive pose with matched limb configurations) would tighten the claim.
+
+Body-type and clothing variations are not factored out across the 25 pairs.
+
+The probe is correlational.
+
+## License
+
+Apache 2.0 — matches base FLUX.2 Klein 4B.
+
+## References
+
+- Gabeur, V., Long, S., Peng, S., et al. *Image Generators are Generalist Vision Learners.* [arXiv:2604.20329](https://arxiv.org/abs/2604.20329) (2026).
+- Black Forest Labs. *FLUX.2 Klein.* https://bfl.ai/models/flux-2-klein (2025).