Training-free concept sliders (arxiv 2511.00103) in noise-prediction space,
sourced from the learned taste (user's choice). New score_slider + strength(eta)
inputs on the Scene Chain sampler, additive/non-disruptive, reusing the same
learned liked direction + embed_guidance_source + refinement_key_input.
Mechanism: a per-scene model_function_wrapper synthesizes a +/- concept pair from
the base conditioning and the liked direction (no concept prompts), runs 3 forward
passes on quality-phase steps and combines eps_mod = eps_base + eta*(eps_+ - eps_-).
High-sigma steps stay single-pass = the paper's k-step base-only warmup, keeping the
2x cost off early steps. Video-only: +/- conds are audio-protected and the eps blend
is masked to the packed video stream. Composes with embed_guidance (its nudge cancels
in the +/- difference). Installed/torn down per scene in reverse order; threaded
through batch-training re-dispatch. Fully guarded -> falls back to base prediction.
Tests: tests/test_score_slider.py (5) — combine, warmup, eta=0 no-op, eta linearity.
Pre-existing 16 suite failures unchanged (stale-mock/splitter set); zero new.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
