title: OmniConsistency: Learning Style-Agnostic Consistency from Paired
    Stylization Data
summary: 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.
paper_authors._id: 68354726f57f43667ec539d8
paper_authors.name: Yiren Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T10:34:58.916Z
paper_authors._id: 64311a95034ecbefddd141ef
paper_authors.avatarUrl: /avatars/b6dc5ca373bedbaa368208517954c375.svg
paper_authors.fullname: Yiren Song
paper_authors.type: user
paper_authors.user: yiren98
paper_authors._id: 68354726f57f43667ec539d9
paper_authors.name: Cheng Liu
paper_authors._id: 68354726f57f43667ec539da
paper_authors.name: Mike Zheng Shou
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-27T05:01:31.829Z
paper_authors._id: 63a55320ce5763e06f78519c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1671779060549-noauth.jpeg
paper_authors.fullname: Mike Shou
paper_authors.type: user
paper_authors.user: mikeshou
paper_title: OmniConsistency: Learning Style-Agnostic Consistency from Paired
    Stylization Data
paper_summary: 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.
paper_ai_summary: OmniConsistency, using large-scale Diffusion Transformers, enhances stylization consistency and generalization in image-to-image pipelines without style degradation.
paper_ai_keywords: diffusion models OmniConsistency Diffusion Transformers DiTs in-context consistency learning two-stage progressive learning style LoRAs Flux framework