PFlow-T: A Persistence-Driven Forward Process for Topology-Controlled Generation
For researchers in generative modeling and topological data analysis, PFlow-T provides a novel approach to topology-controlled generation, though it is currently limited to low-resolution pixel-space proxies.
PFlow-T introduces a generative model that replaces Gaussian noise with persistent homology to corrupt topological features (e.g., holes) during the forward process, enabling direct inversion to generate samples with controlled topology. On MNIST digits, it significantly outperforms baselines in generating requested Betti numbers and handling out-of-distribution tasks.
Current topology aware diffusion models face an architectural mismatch by using Gaussian noise for corruption while recovering structural features through conditional side channels To fix this we introduce PFlow T a generative model that bases its forward process entirely on persistent homology In PFlow T time measures the destruction of H1 topological features like holes rather than Gaussian noise injection This forward process eliminates features based on their persistence The reverse network then directly inverts this structured corruption to predict the clean state in one step Tests on MNIST digits zero one and eight show PFlow T significantly outperforms a baseline model in generating requested Betti numbers and handling out of distribution tasks PFlow T is the first generative architecture using persistent homology for the forward process although we note it is currently limited to low resolution pixel space proxies