평범한 필기장

paper : https://arxiv.org/abs/2410.12557 One Step Diffusion via Shortcut ModelsDiffusion models and flow-matching models have enabled generating diverse and realistic images by learning to transfer noise to data. However, sampling from these models involves iterative denoising over many neural network passes, making generation slow aarxiv.orgAbstract본 논문은 shortcut model을 제안한다. 이는 single network를..

Paper : https://arxiv.org/abs/2504.13109 UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow ModelsFlow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are often ineffective or inapplicable to them. The straight-line, non-crossing trajectories of flow models posearxiv.orgAbstractDiffusion mo..

Paper : https://arxiv.org/abs/2411.15843 Unveil Inversion and Invariance in Flow Transformer for Versatile Image EditingLeveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible invariance control mechanism to preserve non-target contents. However, thearxiv.org1. Introductio..

Paper : https://arxiv.org/abs/2411.00113 A Geometric Framework for Understanding Memorization in Generative ModelsAs deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the legal andarxiv.orgAbstract본 논문은 memori..

Paper : https://arxiv.org/abs/2406.03537 A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion ModelsHigh-dimensional data commonly lies on low-dimensional submanifolds, and estimating the local intrinsic dimension (LID) of a datum -- i.e. the dimension of the submanifold it belongs to -- is a longstanding problem. LID can be understood as the number ..