mlsanityData to 14 Oct 2026

StyleComposer: Training-Free Multi-Reference Style Composition

arXiv 2608.05213 ↗First posted 5 Aug 2026cs.CVcited by 0

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Abstract (arXiv)

The style of a painting is not monolithic: color, texture, and structure may come from different sources. Existing reference-guided methods transfer them as one style signal, leaving each attribute's source and strength outside the user's control. We ask where in a diffusion model one attribute can change while the others hold, and find that no single representation isolates all three. The proposed StyleComposer therefore routes each style attribute through the representation where it separates best and coordinates the routes over denoising time. Without training or inversion, it satisfies three references and the prompt jointly more closely than prior methods, and exposes one strength slider per attribute. Project page: https://lexxsh.github.io/StyleComposer

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 5 Aug 2026.

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21 of 27 references.

References · 21

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  3. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
  4. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  5. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  6. 2024Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Predictioncited by 1,023
  7. 2025Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scalingcited by 812
  8. 2023Style Aligned Image Generation via Shared Attentioncited by 216
  9. 2023StyleDrop: Text-to-Image Generation in Any Stylecited by 199
  10. 2023Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfercited by 196
  11. 2024Implicit Style-Content Separation using B-LoRAcited by 135
  12. 2024Measuring Style Similarity in Diffusion Modelscited by 109
9 more
  1. 2025NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scalecited by 57
  2. 2024StyleMaster: Stylize Your Video with Artistic Generation and Translationcited by 47
  3. 2025OmniStyle: Filtering High Quality Style Transfer Data at Scalecited by 30
  4. 2025FreeFlux: Understanding and Exploiting Layer-Specific Roles in RoPE-Based MMDiT for Versatile Image Editingcited by 22
  5. 2024Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measurescited by 14
  6. 2025ConsistEdit: Highly Consistent and Precise Training-free Visual Editingcited by 6
  7. 2025AlignedGen: Aligning Style Across Generated Imagescited by 5
  8. 2026Untwisting RoPE: Frequency Control for Shared Attention in DiTscited by 5
  9. 2026Compressing Image Style Training into a Single Model Forwardcited by 2

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  1. 2025Style Composition within Distinct LoRA modules for Traditional Art
  2. 2022Inversion-Based Style Transfer with Diffusion Models
  3. 2025SCAdapter: Content-Style Disentanglement for Diffusion Style Transfer
  4. 2024Learning Artistic Signatures: Symmetry Discovery and Style Transfer
  5. 2025Training-Free Multi-Style Fusion Through Reference-Based Adaptive Modulation
  6. 2026Scheduled Style Injection: Expanding the Style-Content Pareto Frontier in Training-Free Diffusion-based Style Transfer
  7. 2025StyleSSP: Sampling StartPoint Enhancement for Training-free Diffusion-based Method for Style Transfer
  8. 2026Through Van Gogh's Eyes: Global Style Transfer with Diffusion Model