HAM: A Training-Free Style Transfer Approach via Heterogeneous Attention Modulation for Diffusion Models

• URL: http://arxiv.org/abs/2603.24043v1
• Date: Wed, 25 Mar 2026 07:52:30 GMT
• Title: HAM: A Training-Free Style Transfer Approach via Heterogeneous Attention Modulation for Diffusion Models
• Abstract summary: We propose a training-free style transfer approach via $textbfh$eterogeneous $textbfa$ttention.<n>During the diffusion process, it innovatively employs HAM for different attention mechanisms, including Global Attention Regulation (GAR) and Local Attention Transplantation (LAT)<n>Our approach is validated through a series of qualitative and quantitative experiments, achieving state-of-the-art performance on multiple quantitative metrics.
• Score: 22.24377615990115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have demonstrated remarkable performance in image generation, particularly within the domain of style transfer. Prevailing style transfer approaches typically leverage pre-trained diffusion models' robust feature extraction capabilities alongside external modular control pathways to explicitly impose style guidance signals. However, these methods often fail to capture complex style reference or retain the identity of user-provided content images, thus falling into the trap of style-content balance. Thus, we propose a training-free style transfer approach via $\textbf{h}$eterogeneous $\textbf{a}$ttention $\textbf{m}$odulation ($\textbf{HAM}$) to protect identity information during image/text-guided style reference transfer, thereby addressing the style-content trade-off challenge. Specifically, we first introduces style noise initialization to initialize latent noise for diffusion. Then, during the diffusion process, it innovatively employs HAM for different attention mechanisms, including Global Attention Regulation (GAR) and Local Attention Transplantation (LAT), which better preserving the details of the content image while capturing complex style references. Our approach is validated through a series of qualitative and quantitative experiments, achieving state-of-the-art performance on multiple quantitative metrics.

Related papers

• Sissi: Zero-shot Style-guided Image Synthesis via Semantic-style Integration [57.02757226679549]
  We introduce a training-free framework that reformulates style-guided synthesis as an in-context learning task.<n>We propose a Dynamic Semantic-Style Integration (DSSI) mechanism that reweights attention between semantic and style visual tokens.<n>Experiments show that our approach achieves high-fidelity stylization with superior semantic-style balance and visual quality.
  arXiv  Detail & Related papers  (2026-01-10T16:01:14Z)
• AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models [4.364797586362505]
  AttenST is a training-free attention-driven style transfer framework.<n>We propose a style-guided self-attention mechanism that conditions self-attention on the reference style.<n>We also introduce a dual-feature cross-attention mechanism to fuse content and style features.
  arXiv  Detail & Related papers  (2025-03-10T13:28:36Z)
• Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution [24.88532732093652]
  Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation.<n>In contrast to existing approaches, we have discovered that latent features in vanilla diffusion models inherently contain natural style and content distributions.<n>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.
  arXiv  Detail & Related papers  (2024-11-28T15:56:17Z)
• ZePo: Zero-Shot Portrait Stylization with Faster Sampling [61.14140480095604]
  This paper presents an inversion-free portrait stylization framework based on diffusion models that accomplishes content and style feature fusion in merely four sampling steps. We propose a feature merging strategy to amalgamate redundant features in Consistency Features, thereby reducing the computational load of attention control.
  arXiv  Detail & Related papers  (2024-08-10T08:53:41Z)
• ArtWeaver: Advanced Dynamic Style Integration via Diffusion Model [73.95608242322949]
  Stylized Text-to-Image Generation (STIG) aims to generate images from text prompts and style reference images. We present ArtWeaver, a novel framework that leverages pretrained Stable Diffusion to address challenges such as misinterpreted styles and inconsistent semantics.
  arXiv  Detail & Related papers  (2024-05-24T07:19:40Z)
• DiffStyler: Diffusion-based Localized Image Style Transfer [0.0]
  Image style transfer aims to imbue digital imagery with the distinctive attributes of style targets, such as colors, brushstrokes, shapes. Despite the advancements in arbitrary style transfer methods, a prevalent challenge remains the delicate equilibrium between content semantics and style attributes. This paper introduces DiffStyler, a novel approach that facilitates efficient and precise arbitrary image style transfer.
  arXiv  Detail & Related papers  (2024-03-27T11:19:34Z)
• HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models [84.12784265734238]
  The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. We propose HiCAST, which is capable of explicitly customizing the stylization results according to various source of semantic clues. A novel learning objective is leveraged for video diffusion model training, which significantly improve cross-frame temporal consistency.
  arXiv  Detail & Related papers  (2024-01-11T12:26:23Z)
• Style Injection in Diffusion: A Training-free Approach for Adapting Large-scale Diffusion Models for Style Transfer [19.355744690301403]
  We introduce a novel artistic style transfer method based on a pre-trained large-scale diffusion model without any optimization. Our experimental results demonstrate that our proposed method surpasses state-of-the-art methods in both conventional and diffusion-based style transfer baselines.
  arXiv  Detail & Related papers  (2023-12-11T09:53:12Z)
• $Z^*$: Zero-shot Style Transfer via Attention Rearrangement [27.185432348397693]
  This study shows that vanilla diffusion models can directly extract style information and seamlessly integrate the generative prior into the content image without retraining. We adopt dual denoising paths to represent content/style references in latent space and then guide the content image denoising process with style latent codes.
  arXiv  Detail & Related papers  (2023-11-25T11:03:43Z)
• MODIFY: Model-driven Face Stylization without Style Images [77.24793103549158]
  Existing face stylization methods always acquire the presence of the target (style) domain during the translation process. We propose a new method called MODel-drIven Face stYlization (MODIFY), which relies on the generative model to bypass the dependence of the target images. Experimental results on several different datasets validate the effectiveness of MODIFY for unsupervised face stylization.
  arXiv  Detail & Related papers  (2023-03-17T08:35:17Z)
• DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization [66.42741426640633]
  DiffStyler is a dual diffusion processing architecture to control the balance between the content and style of diffused results. We propose a content image-based learnable noise on which the reverse denoising process is based, enabling the stylization results to better preserve the structure information of the content image.
  arXiv  Detail & Related papers  (2022-11-19T12:30:44Z)
• Diffusion-based Image Translation using Disentangled Style and Content Representation [51.188396199083336]
  Diffusion-based image translation guided by semantic texts or a single target image has enabled flexible style transfer. It is often difficult to maintain the original content of the image during the reverse diffusion. We present a novel diffusion-based unsupervised image translation method using disentangled style and content representation. Our experimental results show that the proposed method outperforms state-of-the-art baseline models in both text-guided and image-guided translation tasks.
  arXiv  Detail & Related papers  (2022-09-30T06:44:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.