Qwen text reaches the DiT through condition_proj + a 2-block token
refiner ending in an RMSNorm. Everything steered today happens BEFORE
that, so the refiner mixes the edit across tokens and its final norm
renormalizes the magnitude away — what lands is not what was learned.
TokenRefinerEdit patches the refiner's output instead: a scale and/or a
bias over a row range, landing unchanged in the space the 50 blocks read.
project_into_refiner_space maps a conditioning-space direction across with
condition_proj's weight only (a direction is a difference of points, so
the bias would move the origin).
Ships h3_prompt_scale: how loudly the prompt is read through attention,
distinct from h3_gain_prompt (how much each block writes back into those
rows). Confined to the prompt rows via minimax_token_tags, so a wired
reference's vision block is left alone. Free, 1.0 = model not cloned.
The bias path is the mechanism for a learned direction and is not wired to
one yet: a single [hidden] vector added to every prompt row is
prompt-length-independent, which is the shape a learned direction needs
here since Qwen does not pad.
digital-garbage
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