Generator Matching: Generative modeling with arbitrary Markov processes
Peter Holderrieth Marton Havasi Jason Yim Neta Shaul Itai Gat Tommi Jaakkola Brian Karrer Ricky T. Q. Chen Yaron Lipman
We introduce Generator Matching, a modality-agnostic framework for generative modeling using arbitrary Markov processes. Generators characterize the infinitesimal evolution of a Markov process, which we leverage for generative modeling in a similar vein to flow matching: we construct conditional generators which generate single data points, then learn to approximate the marginal generator which generates the full data distribution. We show that Generator Matching unifies various generative modeling methods, including diffusion models, flow matching and discrete diffusion models. Furthermore, it expands the design space to new and unexplored Markov processes such as jump processes. Finally, Generator Matching enables the construction of superpositions of Markov generative models and enables the construction of multimodal models in a rigorous manner. We empirically validate our method on image and multimodal generation, e.g. showing that superposition with a jump process improves performance.
Flow matching, Markov process, Diffusion model, Generative Modeling
The core principles of flow matching can be vastly generalized to practically all continuous-time Markov processes using Markov generators, unifying all previous methods and opening the door to new generative models agnostic to data modality.