# OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data

arXiv: https://arxiv.org/abs/2505.18445 · HTML: https://mlsanity.com/paper/2505.18445/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 43 of its 53 references that are in the map.
- First posted on arXiv 24 May 2025.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 43)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [OminiControl: Minimal and Universal Control for Diffusion Transformer](https://mlsanity.com/paper/2411.15098/) (2024)
- [DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data](https://mlsanity.com/paper/2306.09344/) (2023)
- [Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://mlsanity.com/paper/2405.08748/) (2024)
- [ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs](https://mlsanity.com/paper/2311.13600/) (2023)
- [InstantStyle: Free Lunch towards Style-Preserving in Text-to-Image Generation](https://mlsanity.com/paper/2404.02733/) (2024)
- [SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation](https://mlsanity.com/paper/2312.16272/) (2023)
- [Implicit Style-Content Separation using B-LoRA](https://mlsanity.com/paper/2403.14572/) (2024)
- [EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer](https://mlsanity.com/paper/2503.07027/) (2025)
- [Any2AnyTryon: Leveraging Adaptive Position Embeddings for Versatile Virtual Clothing Tasks](https://mlsanity.com/paper/2501.15891/) (2025)
- [OminiControl2: Efficient Conditioning for Diffusion Transformers](https://mlsanity.com/paper/2503.08280/) (2025)
- [StyleAlign: Analysis and Applications of Aligned StyleGAN Models](https://mlsanity.com/paper/2110.11323/) (2021)

## Built on by (most cited first, 30 of 32)

- [UniWorld-V1: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation](https://mlsanity.com/paper/2506.03147/) (2025)
- [RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers](https://mlsanity.com/paper/2506.02528/) (2025)
- [FastVMT: Eliminating Redundancy in Video Motion Transfer](https://mlsanity.com/paper/2602.05551/) (2026)
- [Personalized Vision via Visual In-Context Learning](https://mlsanity.com/paper/2509.25172/) (2025)
- [ThinkGen: Generalized Thinking for Visual Generation](https://mlsanity.com/paper/2512.23568/) (2025)
- [WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation](https://mlsanity.com/paper/2511.22098/) (2025)
- [StreamingEffect: Real-Time Human-Centric Video Effect Generation](https://mlsanity.com/paper/2605.17019/) (2026)
- [VISTA: Triplet-Supervised Video Style Transfer with Diffusion Transformers](https://mlsanity.com/paper/2605.17312/) (2026)
- [FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning](https://mlsanity.com/paper/2511.17979/) (2025)
- [TeleStyle: Content-Preserving Style Transfer in Images and Videos](https://mlsanity.com/paper/2601.20175/) (2026)
- [NumeriKontrol: Adding Numeric Control to Diffusion Transformers for Instruction-based Image Editing](https://mlsanity.com/paper/2511.23105/) (2025)
- [OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation](https://mlsanity.com/paper/2605.12038/) (2026)
- [PickStyle: Video-to-Video Style Transfer with Context-Style Adapters](https://mlsanity.com/paper/2510.07546/) (2025)
- [CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching](https://mlsanity.com/paper/2511.21024/) (2025)
- [ReMoT: Reinforcement Learning with Motion Contrast Triplets](https://mlsanity.com/paper/2603.00461/) (2026)
- [CollectionLoRA: Collecting 50 Effects in 1 LoRA via Multi-Teacher On-Policy Distillation](https://mlsanity.com/paper/2605.25378/) (2026)
- [MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks](https://mlsanity.com/paper/2509.14638/) (2025)
- [ViFeEdit: A Video-Free Tuner of Your Video Diffusion Transformer](https://mlsanity.com/paper/2603.15478/) (2026)
- [Unified Long Video Inpainting and Outpainting via Overlapping High-Order Co-Denoising](https://mlsanity.com/paper/2511.03272/) (2025)
- [TokenPure: Watermark Removal through Tokenized Appearance and Structural Guidance](https://mlsanity.com/paper/2512.01314/) (2025)
- [OmniDiT: Extending Diffusion Transformer to Omni-VTON Framework](https://mlsanity.com/paper/2603.19643/) (2026)
- [EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing](https://mlsanity.com/paper/2605.07455/) (2026)
- [SWEET: Sparse World Modeling with Image Editing for Embodied Task Execution](https://mlsanity.com/paper/2605.19319/) (2026)
- [Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning](https://mlsanity.com/paper/2606.14746/) (2026)
- [Ghosts Beneath Textures: Texture-Relation Cues for Cross-Paradigm AI-Generated Image Detection](https://mlsanity.com/paper/2607.03862/) (2026)
- [GenPuzzle: Benchmarking Visual Reasoning in Image Generation Models](https://mlsanity.com/paper/2609.05902/) (2026)
- [UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement](https://mlsanity.com/paper/2604.17850/) (2026)
- [Causal Probing for Internal Visual Representations in Multimodal Large Language Models](https://mlsanity.com/paper/2605.05593/) (2026)
- [TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillation](https://mlsanity.com/paper/2606.20709/) (2026)
- [Staying True to the Origin: Continuous Image Stylization with Smooth Transitions](https://mlsanity.com/paper/2608.08125/) (2026)

## Abstract (arXiv)

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.