New sampler targeting LTX2.3 distilled LoRA and similar 4-8 step
flow-matching distilled video models. Three improvements over plain Euler:
Adams-Bashforth 2-step multistep (order=2, default):
Extrapolates the denoised direction from two consecutive steps using
variable-step-size AB2 coefficients: (1 + r/2) * d_curr - (r/2) * d_prev
where r = h_current / h_previous. Gives second-order accuracy across the
large sigma jumps typical of distilled schedules at zero extra model-call cost.
Heun predictor-corrector on final steps (final_correction_steps=1, default):
Replaces the last N steps with a Heun pass: Euler predictor to sigma_next,
second model call at sigma_next for the corrector direction, averaged update.
Costs one extra model call per corrected step and significantly improves
sharpness and detail in the steps that define the final output.
Multistep history is invalidated after each Heun step.
Optional s_noise (default 0.0 = deterministic):
Small ancestral-style noise injection (scaled by sigma_up) for diversity.
Distilled models are trained as pure ODE; any non-zero value is an
intentional deviation. Keep below 0.15 if used at all.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
