Training Latent Diffusion Models with Interacting Particle Algorithms

We introduce a novel particle-based algorithm for end-to-end training of latent diffusion models. We reformulate the training task as minimizing a free energy functional and obtain a gradient flow that does so. By approximating the latter with a system of interacting particles, we obtain the algorithm, which we underpin it theoretically by providing error guarantees. The novel algorithm compares favorably in experiments with previous particle-based methods and variational inference analogues.

@article{wang2025_2505.12412,
  title={ Training Latent Diffusion Models with Interacting Particle Algorithms },
  author={ Tim Y. J. Wang and Juan Kuntz and O. Deniz Akyildiz },
  journal={arXiv preprint arXiv:2505.12412},
  year={ 2025 }
}