3 papers
cs.LG2025
Test-time scaling of diffusions with flow maps
Amirmojtaba Sabour, Michael S. Albergo, Carles Domingo-Enrich +4
A common recipe to improve diffusion models at test-time so that samples score highly against a user-specified reward is to introduce the gradient of the reward into the dynamics o…
cs.CV2025
Align Your Flow: Scaling Continuous-Time Flow Map Distillation
Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
Diffusion- and flow-based models have emerged as state-of-the-art generative modeling approaches, but they require many sampling steps. Consistency models can distill these models…
cs.LG2025
Score-based Diffusion Models in Function Space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista +10
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a r…