Overclocking Electrostatic Generative Models

• URL: http://arxiv.org/abs/2509.22454v1
• Date: Fri, 26 Sep 2025 15:07:23 GMT
• Title: Overclocking Electrostatic Generative Models
• Authors: Daniil Shlenskii, Alexander Korotin,
• Abstract summary: PFGM++ operates in an extended data space with auxiliary dimensionality $D$, recovering the diffusion model framework as $Dtoinfty$.<n>We propose Inverse Poisson Flow Matching (IPFM), a novel distillation framework that accelerates electrostatic generative models across all values of $D$.<n>IPFM produces distilled generators that achieve near-teacher or even superior sample quality using only a few function evaluations.
• Score: 59.271136356755996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electrostatic generative models such as PFGM++ have recently emerged as a powerful framework, achieving state-of-the-art performance in image synthesis. PFGM++ operates in an extended data space with auxiliary dimensionality $D$, recovering the diffusion model framework as $D\to\infty$, while yielding superior empirical results for finite $D$. Like diffusion models, PFGM++ relies on expensive ODE simulations to generate samples, making it computationally costly. To address this, we propose Inverse Poisson Flow Matching (IPFM), a novel distillation framework that accelerates electrostatic generative models across all values of $D$. Our IPFM reformulates distillation as an inverse problem: learning a generator whose induced electrostatic field matches that of the teacher. We derive a tractable training objective for this problem and show that, as $D \to \infty$, our IPFM closely recovers Score Identity Distillation (SiD), a recent method for distilling diffusion models. Empirically, our IPFM produces distilled generators that achieve near-teacher or even superior sample quality using only a few function evaluations. Moreover, we observe that distillation converges faster for finite $D$ than in the $D \to \infty$ (diffusion) limit, which is consistent with prior findings that finite-$D$ PFGM++ models exhibit more favorable optimization and sampling properties.

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