# Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution

arXiv: https://arxiv.org/abs/2411.19231 · HTML: https://mlsanity.com/paper/2411.19231/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2024.

## Built on by (most cited first, 4 of 4)

- [Untwisting RoPE: Frequency Control for Shared Attention in DiTs](https://mlsanity.com/paper/2602.05013/) (2026)
- [Training-free Stylized Text-to-Image Generation with Fast Inference](https://mlsanity.com/paper/2505.19063/) (2025)
- [Inversion-Free Style Transfer with Dual Rectified Flows](https://mlsanity.com/paper/2511.20986/) (2025)
- [Sissi: Zero-shot Style-guided Image Synthesis via Semantic-style Integration](https://mlsanity.com/paper/2601.06605/) (2026)

## Abstract (arXiv)

Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation. Conventional methods employ style loss, derived from second-order statistics or contrastive learning, to constrain style representation in the stylized result. However, these pre-defined style representations often limit stylistic expression, leading to artifacts. In contrast to existing approaches, we have discovered that latent features in vanilla diffusion models inherently contain natural style and content distributions. This allows for direct extraction of style information and seamless integration of generative priors into the content image without necessitating retraining. Our method adopts dual denoising paths to represent content and style references in latent space, subsequently guiding the content image denoising process with style latent codes. We introduce a Cross-attention Reweighting module that utilizes local content features to query style image information best suited to the input patch, thereby aligning the style distribution of the stylized results with that of the style image. Furthermore, we design a scaled adaptive instance normalization to mitigate inconsistencies in color distribution between style and stylized images on a global scale. Through theoretical analysis and extensive experimentation, we demonstrate the effectiveness and superiority of our diffusion-based ulinezero-shot ulinestyle ulinetransfer via ulineadjusting style distulineribution, termed Z-STAR+.