arXiv:2606. 13240v1 Announce Type: cross Abstract: A key strength of diffusion models lies in their flexibility, since their outputs can be controlled at sampling time through guidance.
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arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.
arXiv:2608. 19504v1 Announce Type: new Abstract: We propose a conditioning mechanism for diffusion models based on multi-speed joint diffusion of the target and the condition.
Diffusion models are increasingly used as controllable samplers, whose generations can be steered at inference time according to a chosen reward function. While such rewards are typically defined on individual samples, for many applications it is desirable to steer according to distribution-level rewards, for example to calibrate with population-level information or to encourage diversity.
arXiv:2608. 08770v1 Announce Type: cross Abstract: Diffusion models are increasingly used as controllable samplers, whose generations can be steered at inference time according to a chosen reward function.
The paper introduces a new method for conditioning degenerate diffusion models, which are generative models that rely on diffusion processes with singular diffusion coefficients. Traditional approaches use score functions for guidance, but this work employs causal optimal transport to define approximate loss functions that can identify a minimum‑entropy control even when conditional densities are non‑existent or non‑smooth. The method hinges on the predictable representation property of conditioned diffusion processes and the well‑posedness of their martingale problem, following the framework of "Ust"unel.
The paper addresses the challenge of guiding conditioned generative models that are diffusion processes with singular diffusion coefficients, where traditional conditional densities may be nonexistent or non‑smooth. It proposes using causal optimal transport to construct approximate loss functions that identify a minimum‑entropy control for guidance, relying on the predictable representation property of conditioned diffusion processes and well‑posed martingale problems à la Üstünel.