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