DistillKac: Few-Step Image Generation via Damped Wave Equations

ICLR 2026 Conference Submission13819 Authors

Keywords: generative models, Kac flow, damped wave equation, telegrapher equation, finite-speed probability flow, classifier-free guidance, endpoint distillation, few-step sampling

Abstract: We present DistillKac, a fast image generator that uses the damped wave equation and its stochastic Kac representation to move probability mass at finite speed. In contrast to diffusion models whose reverse time velocities can become stiff and implicitly allow unbounded propagation speed, Kac dynamics enforce finite speed transport and yield globally bounded kinetic energy. Building on this structure, we introduce classifier free guidance in velocity space that preserves square integrability under mild conditions. We then propose endpoint only distillation that trains a student to match a frozen teacher over long intervals. We prove a stability result that promotes supervision at the endpoints to closeness along the entire path. Experiments demonstrate DistillKac delivers high quality samples with very few function evaluations while retaining the numerical stability benefits of finite speed probability flows.

Primary Area: generative models

Submission Number: 13819