SymDiff: Equivariant Diffusion via Stochastic Symmetrisation
Keywords: Equivariance, Diffusion Models, Symmetrisation, Molecular Generation, Markov Categories
TL;DR: We propose SymDiff, a novel method for constructing equivariant diffusion models using the recently introduced framework of stochastic symmetrisation.
Abstract: We propose SYMDIFF, a novel method for constructing equivariant diffusion
models using the recently introduced framework of stochastic symmetrisation.
SYMDIFF resembles a learned data augmentation that is deployed at sampling
time, and is lightweight, computationally efficient, and easy to implement on
top of arbitrary off-the-shelf models. Notably, in contrast to previous work,
SYMDIFF typically does not require any neural network components that are
intrinsically equivariant, avoiding the need for complex parameterizations and the
use of higher-order geometric features. Instead, our method can leverage highly
scalable modern architectures as drop-in replacements for these more constrained
alternatives. We show that this additional flexibility yields significant empirical
benefit on E(3)-equivariant molecular generation. To the best of our knowledge,
this is the first application of symmetrisation to generative modelling, suggesting
its potential in this domain more generally.
Supplementary Material: pdf
Primary Area: generative models
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Submission Number: 12205
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