UniEdit-Flow presents tuning-free Uni-Inv and Uni-Edit methods for inversion and editing in flow models that achieve accurate reconstruction and robust region-preserving edits across generative models.
Lafite: Latent diffu- sion model with feature editing for unsupervised multi-class anomaly detection
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DPDiff-AD conditions a diffusion model on local prototypes (via nearest aggregation) and global prototypes (via optimal transport) to model normality scalably in multi-class anomaly detection, reporting AUROC gains on 160-category data.
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UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
UniEdit-Flow presents tuning-free Uni-Inv and Uni-Edit methods for inversion and editing in flow models that achieve accurate reconstruction and robust region-preserving edits across generative models.
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Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces
DPDiff-AD conditions a diffusion model on local prototypes (via nearest aggregation) and global prototypes (via optimal transport) to model normality scalably in multi-class anomaly detection, reporting AUROC gains on 160-category data.