Schedule-Free Denoising Diffusion Sampling using Time Scores

Annealing-based neural samplers amortize sampling from unnormalized target distributions by transporting samples along a family of intermediate smoothed densities connecting a tractable source distribution to the target. Existing approaches typically rely on a predefined noise schedule, prescribing the progression through the intermediate densities independently of the current sample state. To remedy this, we propose Dual Score Diffusion Sampling (DSDS), which instead treats the diffusion time, particularly its associated noise varaince, as a stochastic variable and lifts the sampling problem to the joint space. In this lifted space, both variables evolve jointly through stochastic dynamics, which allows the sampler to adaptively navigate the joint probability space. Across synthetic and image-generation experiments, we show that this leads to improved robustness, while remaining competitive with schedule-based samplers.