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name: stochastic_interpolant_sampler description: Integrate bounded stochastic-interpolant probability-flow ODE and simple diffusion sampler updates from learned velocity and score fields.

Stochastic Interpolant Sampler

Use this skill after velocity and score or denoiser fields have been defined. It provides bounded deterministic ODE and seeded Euler-Maruyama style SDE updates for recovery experiments.

Inputs

• Initial scalar samples or sample summaries.
• A velocity function b(t, x).
• Optional score function s(t, x).
• A scalar diffusion coefficient epsilon and step count.
• A deterministic seed for stochastic updates.

Outputs

• Final generated samples.
• Trajectory summaries for initial and final states.
• Solver metadata tying the result to ODE or SDE dynamics.

Workflow

1. Use ODE mode for the main reduced recovery unless stochastic diffusion behavior is being ablated.
2. Apply Euler updates on a bounded uniform grid.
3. For SDE mode, use drift b + epsilon*s and seeded Gaussian increments.
4. Treat epsilon=0 as deterministic and verify it matches ODE drift-only behavior.
5. Log step count and sample summaries for validation.

Validation

Run:

python scripts/sampler.py --demo
python tests/test_sampler.py

Limitations

The script is intentionally small and deterministic. It is not a substitute for the paper's adaptive Dormand-Prince or Heun samplers, but it preserves the probability-flow and tunable-diffusion contracts for bounded recovery.