Producer (Studio owns conditioning, per the boundary law): refine_v2 gains batch_variants.
When >1, _v2_build_batch_variants packs N conditioning entries into the single positive wire,
each tagged 'funpack_batch_variant'. Variant 0 is the fully-processed base; variants 1..N-1
re-resolve shortcuts with seed+i (random option per entry) and get the same conditioning-memory
treatment, deduped by expansion text. No shortcut variability -> all entries resolve identical
and reuse the base cond -> the batch becomes seed-only (sampler varies the seed per entry).
Captures the pre-shortcut prompt to re-resolve. Not combined with split_by_transitions.
Trigger: a 'Batch Training' button on the Studio NODE (next to Open Studio) — prompts for N,
sets refiner.batch_variants, queues one prompt, then auto-reverts to 1 (one-shot). Studio is
the batch trigger (hub principle); no sampler input needed.
Sampler: now enters batch mode when positive carries variant markers, even if batch_iterations
is 1 (markers are the trigger; variant count wins). Each entry already saves its own cond, so
Submit trains the VF per variant — teaching which prompt variant rated better. Panel hints now
point at the button.
End-to-end: press Batch Training -> N -> Studio makes N variant conds -> sampler samples N ->
previews to temp -> rate in Studio panel -> Submit trains VF per entry -> batch cleared.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
