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The paper introduces Categorical Flow Maps, a flow-matching method designed for fast, few-step generation of categorical data using self-distillation. By defining a continuous flow map towards the simplex, the method transports probability mass to a predicted endpoint, enabling the use of distillation techniques and a novel endpoint consistency objective. Experiments demonstrate state-of-the-art few-step generation performance across images, molecular graphs, and text, even achieving strong results in single-step generation.
Achieve state-of-the-art few-step generation on images, molecular graphs, and text with Categorical Flow Maps, a self-distillation approach that unlocks continuous-time inference for categorical data.
We introduce Categorical Flow Maps, a flow-matching method for accelerated few-step generation of categorical data via self-distillation. Building on recent variational formulations of flow matching and the broader trend towards accelerated inference in diffusion and flow-based models, we define a flow map towards the simplex that transports probability mass toward a predicted endpoint, yielding a parametrisation that naturally constrains model predictions. Since our trajectories are continuous rather than discrete, Categorical Flow Maps can be trained with existing distillation techniques, as well as a new objective based on endpoint consistency. This continuous formulation also automatically unlocks test-time inference: we can directly reuse existing guidance and reweighting techniques in the categorical setting to steer sampling toward downstream objectives. Empirically, we achieve state-of-the-art few-step results on images, molecular graphs, and text, with strong performance even in single-step generation.
