Spectral Guidance learns singular functions via self-supervised objective to project guidance signals onto diffusion sampling trajectories, enabling stable control without retraining or backpropagation and improving CIFAR-10 accuracy by 37 points with 4x faster sampling.
Florian Handke, Dejan Stanˇcevi´c, Felix Koulischer, Thomas Demeester, and Luca Ambrogioni
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Symmetry breaking and nonlocality phase transitions occur nearly simultaneously during diffusion model generation in modern transformers.
citing papers explorer
-
Spectral Guidance for Flexible and Efficient Control of Diffusion Models
Spectral Guidance learns singular functions via self-supervised objective to project guidance signals onto diffusion sampling trajectories, enabling stable control without retraining or backpropagation and improving CIFAR-10 accuracy by 37 points with 4x faster sampling.
-
Concurrence of Symmetry Breaking and Nonlocality Phase Transitions in Diffusion Models
Symmetry breaking and nonlocality phase transitions occur nearly simultaneously during diffusion model generation in modern transformers.