HSI: A Holistic Style Injector for Arbitrary Style Transfer
- URL: http://arxiv.org/abs/2502.04369v1
- Date: Wed, 05 Feb 2025 09:36:24 GMT
- Title: HSI: A Holistic Style Injector for Arbitrary Style Transfer
- Authors: Shuhao Zhang, Hui Kang, Yang Liu, Fang Mei, Hongjuan Li,
- Abstract summary: Holistic Style (HSI) is a novel attention-style transformation module to deliver artistic expression of target style.
HSI performs stylization only based on global style representation that is more in line with the characteristics of style transfer.
Our method outperforms state-of-the-art approaches in both effectiveness and efficiency.
- Score: 8.47567292281412
- License:
- Abstract: Attention-based arbitrary style transfer methods have gained significant attention recently due to their impressive ability to synthesize style details. However, the point-wise matching within the attention mechanism may overly focus on local patterns such that neglect the remarkable global features of style images. Additionally, when processing large images, the quadratic complexity of the attention mechanism will bring high computational load. To alleviate above problems, we propose Holistic Style Injector (HSI), a novel attention-style transformation module to deliver artistic expression of target style. Specifically, HSI performs stylization only based on global style representation that is more in line with the characteristics of style transfer, to avoid generating local disharmonious patterns in stylized images. Moreover, we propose a dual relation learning mechanism inside the HSI to dynamically render images by leveraging semantic similarity in content and style, ensuring the stylized images preserve the original content and improve style fidelity. Note that the proposed HSI achieves linear computational complexity because it establishes feature mapping through element-wise multiplication rather than matrix multiplication. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches in both effectiveness and efficiency.
Related papers
- StyleRWKV: High-Quality and High-Efficiency Style Transfer with RWKV-like Architecture [29.178246094092202]
Style transfer aims to generate a new image preserving the content but with the artistic representation of the style source.
Most of the existing methods are based on Transformers or diffusion models, however, they suffer from quadratic computational complexity and high inference time.
We present a novel framework StyleRWKV, to achieve high-quality style transfer with limited memory usage and linear time complexity.
arXiv Detail & Related papers (2024-12-27T09:01:15Z) - 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) - Rethink Arbitrary Style Transfer with Transformer and Contrastive Learning [11.900404048019594]
In this paper, we introduce an innovative technique to improve the quality of stylized images.
Firstly, we propose Style Consistency Instance Normalization (SCIN), a method to refine the alignment between content and style features.
In addition, we have developed an Instance-based Contrastive Learning (ICL) approach designed to understand relationships among various styles.
arXiv Detail & Related papers (2024-04-21T08:52:22Z) - A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive
Learning [84.8813842101747]
Unified Contrastive Arbitrary Style Transfer (UCAST) is a novel style representation learning and transfer framework.
We present an adaptive contrastive learning scheme for style transfer by introducing an input-dependent temperature.
Our framework consists of three key components, i.e., a parallel contrastive learning scheme for style representation and style transfer, a domain enhancement module for effective learning of style distribution, and a generative network for style transfer.
arXiv Detail & Related papers (2023-03-09T04:35:00Z) - All-to-key Attention for Arbitrary Style Transfer [98.83954812536521]
We propose a novel all-to-key attention mechanism -- each position of content features is matched to stable key positions of style features.
The resultant module, dubbed StyA2K, shows extraordinary performance in preserving the semantic structure and rendering consistent style patterns.
arXiv Detail & Related papers (2022-12-08T06:46:35Z) - Learning Graph Neural Networks for Image Style Transfer [131.73237185888215]
State-of-the-art parametric and non-parametric style transfer approaches are prone to either distorted local style patterns due to global statistics alignment, or unpleasing artifacts resulting from patch mismatching.
In this paper, we study a novel semi-parametric neural style transfer framework that alleviates the deficiency of both parametric and non-parametric stylization.
arXiv Detail & Related papers (2022-07-24T07:41:31Z) - Parameter-Free Style Projection for Arbitrary Style Transfer [64.06126075460722]
This paper proposes a new feature-level style transformation technique, named Style Projection, for parameter-free, fast, and effective content-style transformation.
This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer.
arXiv Detail & Related papers (2020-03-17T13:07:41Z)
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.