Visual diffusion models have revolutionized the field of creative AI, producing high-quality
and diverse content. However, they inevitably memorize training images or videos …

As diffusion probabilistic models (DPMs) are being employed as mainstream models for
Generative Artificial Intelligence (GenAI), the study of their memorization of training data has …

Diffusion models, widely used for image and video generation, face a significant limitation:
the risk of memorizing and reproducing training data during inference, potentially generating …

Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their
impressive advancements in image generation. However, their growing popularity has …

In this paper, we identify and leverage a novelbright ending'(BE) anomaly in diffusion
models prone to memorizing training images to address a new task: locating localized …

The diffusion model has gained significant attention due to its remarkable data generation
ability in fields such as image synthesis. However, its strong memorization and replication …

Diffusion models have emerged as a powerful framework for generative modeling. At the
heart of the methodology is score matching: learning gradients of families of log-densities for …

Diffusion models (DMs) produce very detailed and high-quality images. Their power results
from extensive training on large amounts of data, usually scraped from the internet without …

In this paper, we introduce a geometric framework to analyze memorization in diffusion
models using the eigenvalues of the Hessian of the log probability density. We propose that …

Privacy vulnerabilities in LLMs, such as leakage from memorization, have been constantly
identified, and various mitigation proposals have been proposed. LoRA is usually used in …