Diverse Sampling in Diffusion Models with Divergence-Free Particle Guidance
Modern generative models can produce high-quality samples, but independent samples under the same condition often yields highly similar outputs. Particle-based methods increase diversity by letting the samples in a batch interact, typically through a repulsive force. These forces, however, also push each sample away from the data distribution and produce visible artifacts. We introduce EDDY, a training-free particle guidance method designed to avoid this failure mode. Instead of a repulsive gradient, EDDY couples particles through anti-symmetric matrix fields passed through a Stein operator, a family of drift perturbations that leaves the Fokker--Planck equation invariant. Unlike repulsiv...
| Authors | Gal Vinograd, Idan Achituve, Ethan Fetaya |
|---|---|
| Primary category | cs.LG |
| Published | 7 May 2026 |
| Last updated at source | 28 September 2026 |
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