Variational Control for Guidance in Diffusion Models
Abstract
Diffusion models exhibit excellent sample quality, but existing guidance methods often require additional model training or are limited to specific tasks. We revisit guidance in diffusion models from the perspective of variational inference and control, introducing Diffusion Trajectory Matching (DTM) that enables guiding pretrained diffusion trajectories to satisfy a terminal cost. DTM unifies a broad class of guidance methods and enables novel instantiations. We introduce a new method within this framework that achieves state-of-the-art results on several linear, non-linear, and blind inverse problems without requiring additional model training or specificity to pixel or latent space diffusion models. Our code will be available at https://github.com/czi-ai/oc-guidance.
Cite
Text
Pandey et al. "Variational Control for Guidance in Diffusion Models." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Pandey et al. "Variational Control for Guidance in Diffusion Models." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/pandey2025icml-variational/)BibTeX
@inproceedings{pandey2025icml-variational,
title = {{Variational Control for Guidance in Diffusion Models}},
author = {Pandey, Kushagra and Sofian, Farrin Marouf and Draxler, Felix and Karaletsos, Theofanis and Mandt, Stephan},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {47755-47780},
volume = {267},
url = {https://mlanthology.org/icml/2025/pandey2025icml-variational/}
}