ray-plantain

ray-plantain

A per-head attention probe of FLUX.2 Klein 4B testing whether the base model has implicitly separated illumination from reflectance — i.e., whether intrinsic-image decomposition emerges as a representational axis without inverse-rendering supervision.

Thesis

Intrinsic-image decomposition (separating the rendered photograph into its physically meaningful albedo, normal, and lighting components) is a classical inverse-rendering problem that has historically required either explicit supervision or carefully designed self-supervision. ray-plantain tests whether the recipe is unnecessary — whether a 4 B image-generation model trained on natural photographs has discovered the rendered-vs-albedo distinction on its own, surfacing it as a per-head axis at base.

Method

Twenty-five paired prompts holding the depicted scene constant. The A condition describes the final rendered photograph (full lighting, shadows, surface reflections). The B condition requests the lighting-free albedo / surface-color component only. 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

Top blocks by max |t|:

• single[0]: max|t|=34.48, 510/768 heads at |t|>3, median |d|=1.00
• joint[0]: max|t|=28.91, 169/192 heads at |t|>3, median |d|=2.54
• joint[4]: max|t|=27.84, 149/192 heads at |t|>3, median |d|=1.32
• joint[3]: max|t|=26.80, 151/192 heads at |t|>3, median |d|=1.22
• joint[1]: max|t|=26.76, 135/192 heads at |t|>3, median |d|=1.10

Interpretation. The strongest plantain probe finding to date by every rigor metric. Over half of all 16,320 attention heads in the model show large-effect-size selectivity (Cohen's d > 0.8) for the rendered/albedo distinction. Split-half consistency r=0.91 places the axis among the most reproducible representational features located so far. The 1,489× ratio over the empirical null at |t|>5 is the highest reported across the plantain probe family.

The result implies that Klein has implicitly performed an inverse-rendering decomposition during pretraining and made the components separately addressable at the per-head level. The maximum-effect block (joint[0]) has 169 of 192 heads at |t|>3 with median Cohen's d above 2.5 — the rendered/albedo distinction is the dominant feature partition for that block. This places a measurable upper bound on how much of the inverse-rendering literature's machinery is actually necessary; the base model already separates illumination from reflectance internally, and the open question shifts to whether the separation is correct (matches physically computed albedo) rather than whether it exists.

Status

Probe complete. No LoRA training; this is a base-model interpretability finding.

Limitations

The probe establishes that Klein has a separable representation of "rendered photograph" vs. "albedo." It does not establish that the predicted albedo is correct — i.e., physically faithful to a held-out lighting decomposition. A natural follow-up generates the predicted albedo at full inference depth and compares to physically computed albedo on a synthetic-image test set with known intrinsics.

The pair count is 25; per-head reproducibility (r=0.91) is high but a larger sweep would tighten estimates and enable per-block confidence intervals.

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 (2026).
• Black Forest Labs. FLUX.2 Klein. https://bfl.ai/models/flux-2-klein (2025).