OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data

Dif challenges fusion models persist: (1) hav maintaining e advanced consistent image stylization stylization significantly in complex, scenes, yet two parti core cularly identity, composition, and fine details, and (2) preventing style degradation in consistenc image-to-image y highlights pipelines the performance with style LoR gap A between s.