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Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network
Grigory Bartosh, David Ruhe, Emiel Hoogeboom +3
Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluation of a heavy neural network.…
Equivariant Neural Diffusion for Molecule Generation
François Cornet, Grigory Bartosh, Mikkel N. Schmidt +1
We introduce Equivariant Neural Diffusion (END), a novel diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Compared to current state-o…
Variational Flow Matching for Graph Generation
Floor Eijkelboom, Grigory Bartosh, Christian Andersson Naesseth +2
We present a formulation of flow matching as variational inference, which we refer to as variational flow matching (VFM). Based on this formulation we develop CatFlow, a flow match…
Neural Diffusion Models
Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth
Diffusion models have shown remarkable performance on many generative tasks. Despite recent success, most diffusion models are restricted in that they only allow linear transformat…