OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data

Yiren Song, Cheng Liu, Mike Shou

OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data: 40 upvotes on Hugging Face Daily Papers, #4 of 76 papers on 2025-05-28. Day-by-day upvote history. It lost 24 votes when the Hub removed votes in bulk.

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 page on Hugging Face · arXiv

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