6 papers
From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime
Luca Ambrogioni, Giulio Franzese, Alberto Foresti +7
How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are based largely on heuristics or…
Noise Scheduling as Information-Guided Allocation in Diffusion Training
Gabriel Raya, Bac Nguyen, Georgios Batzolis +6
We introduce InfoNoise, an online adaptive noise schedule for diffusion training that reallocates optimization effort toward noise levels where denoising is most informative. Toget…
Memorization to Generalization: Emergence of Diffusion Models from Associative Memory
Bao Pham, Gabriel Raya, Matteo Negri +3
Dense Associative Memories (DenseAMs) are generalizations of Hopfield networks, which have superior information storage capacity and can store training data points (memories) at lo…
Losing dimensions: Geometric memorization in generative diffusion
Beatrice Achilli, Enrico Ventura, Gianluigi Silvestri +5
Diffusion models power leading generative AI, but when and how they memorize training data, especially on low-dimensional manifolds, remains unclear. We find memorization emerges g…
Measuring Semantic Information Production in Generative Diffusion Models
Florian Handke, Félix Koulischer, Gabriel Raya +1
It is well known that semantic and structural features of the generated images emerge at different times during the reverse dynamics of diffusion, a phenomenon that has been connec…
Dynamic Negative Guidance of Diffusion Models
Felix Koulischer, Johannes Deleu, Gabriel Raya +2
Negative Prompting (NP) is widely utilized in diffusion models, particularly in text-to-image applications, to prevent the generation of undesired features. In this paper, we show…