OmniConsistency: Learning Style-Agnostic Consistency from Paired
  Stylization Data
Authors: name: Yiren Song, name: Cheng Liu, name: Mike Zheng Shou

Diffusion models have advanced image stylization significantly, yet two core
challenges persist: (1) maintaining consistent stylization in complex scenes,
particularly identity, composition, and fine details, and (2) preventing style
degradation in image-to-image pipelines with style LoRAs. GPT-4o's exceptional
stylization consistency highlights the performance gap between open-source
methods and proprietary models. To bridge this gap, we propose
OmniConsistency, a universal consistency plugin leveraging large-scale
Diffusion Transformers (DiTs). OmniConsistency contributes: (1) an in-context
consistency learning framework trained on aligned image pairs for robust
generalization; (2) a two-stage progressive learning strategy decoupling style
learning from consistency preservation to mitigate style degradation; and (3) a
fully plug-and-play design compatible with arbitrary style LoRAs under the Flux
framework. Extensive experiments show that OmniConsistency significantly
enhances visual coherence and aesthetic quality, achieving performance
comparable to commercial state-of-the-art model GPT-4o.