mlsanityData to 7 Oct 2026

QwenStyle: Content-Preserving Style Transfer with Qwen-Image-Edit

arXiv 2601.06202 ↗First posted 8 Jan 2026cs.CVcited by 4

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to its internal entangled content and style features. In this technical report, we propose the first content-preserving style transfer model trained on Qwen-Image-Edit, which activates Qwen-Image-Edit's strong content preservation and style customization capability. We collected and filtered high quality data of limited specific styles and synthesized triplets with thousands categories of style images in-the-wild. We introduce the Curriculum Continual Learning framework to train QwenStyle with such mixture of clean and noisy triplets, which enables QwenStyle to generalize to unseen styles without degradation of the precise content preservation capability. Our QwenStyle V1 achieves state-of-the-art performance in three core metrics: style similarity, content consistency, and aesthetic quality.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 23 of its 29 references that are in the map. First posted on arXiv 8 Jan 2026.

Builds on

23 of 29 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024StyleTokenizer: Defining Image Style by a Single Instance for Controlling Diffusion Models4 Sep
  2. 2024Measuring Style Similarity in Diffusion Models1 Apr
  3. 2023Forgedit: Text Guided Image Editing via Learning and Forgetting19 Sep
  4. 2023StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models15 Aug

Other references · 19

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
  4. 2022Scalable Diffusion Models with Transformerscited by 5,931
  5. 2022Flow Matching for Generative Modelingcited by 5,426
  6. 2025Qwen2.5-VL Technical Reportcited by 5,418
  7. 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
  8. 2024GPT-4o System Cardcited by 4,467
  9. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
  10. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,094
  11. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  12. 2025Qwen-Image Technical Reportcited by 973
7 more
  1. 2024OminiControl: Minimal and Universal Control for Diffusion Transformercited by 334
  2. 2025OmniGen2: Towards Instruction-Aligned Multimodal Generationcited by 241
  3. 2025EasyControl: Adding Efficient and Flexible Control for Diffusion Transformercited by 118
  4. 2025DreamO: A Unified Framework for Image Customizationcited by 82
  5. 2023StyleAdapter: A Unified Stylized Image Generation Modelcited by 39
  6. 2025OmniStyle: Filtering High Quality Style Transfer Data at Scalecited by 30
  7. 2025CDST: Color Disentangled Style Transfer for Universal Style Reference Customizationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026TeleStyle: Content-Preserving Style Transfer in Images and Videoscited by 5
  2. 2026Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learningcited by 1
  3. 2026UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglementcited by 0
  4. 2026TeleStyle V2: Beyond Content-Preserving Style Transfer with Self-Distillation and Distribution-Matching-Distillationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Scheduled Style Injection: Expanding the Style-Content Pareto Frontier in Training-Free Diffusion-based Style Transfer
  2. 2023Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer
  3. 2024DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer
  4. 2024Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution
  5. 2025AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models
  6. 2026Staying True to the Origin: Continuous Image Stylization with Smooth Transitions
  7. 2025SCAdapter: Content-Style Disentanglement for Diffusion Style Transfer
  8. 2025Training-Free Multi-Style Fusion Through Reference-Based Adaptive Modulation