Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt

• URL: http://arxiv.org/abs/2404.11474v2
• Date: Mon, 29 Apr 2024 11:59:10 GMT
• Title: Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt
• Authors: Zhanjie Zhang, Quanwei Zhang, Huaizhong Lin, Wei Xing, Juncheng Mo, Shuaicheng Huang, Jinheng Xie, Guangyuan Li, Junsheng Luan, Lei Zhao, Dalong Zhang, Lixia Chen,
• Abstract summary: Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. We propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST. Our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods.
• Score: 12.27693060663517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artistic style transfer aims to transfer the learned artistic style onto an arbitrary content image, generating artistic stylized images. Existing generative adversarial network-based methods fail to generate highly realistic stylized images and always introduce obvious artifacts and disharmonious patterns. Recently, large-scale pre-trained diffusion models opened up a new way for generating highly realistic artistic stylized images. However, diffusion model-based methods generally fail to preserve the content structure of input content images well, introducing some undesired content structure and style patterns. To address the above problems, we propose a novel pre-trained diffusion-based artistic style transfer method, called LSAST, which can generate highly realistic artistic stylized images while preserving the content structure of input content images well, without bringing obvious artifacts and disharmonious style patterns. Specifically, we introduce a Step-aware and Layer-aware Prompt Space, a set of learnable prompts, which can learn the style information from the collection of artworks and dynamically adjusts the input images' content structure and style pattern. To train our prompt space, we propose a novel inversion method, called Step-ware and Layer-aware Prompt Inversion, which allows the prompt space to learn the style information of the artworks collection. In addition, we inject a pre-trained conditional branch of ControlNet into our LSAST, which further improved our framework's ability to maintain content structure. Extensive experiments demonstrate that our proposed method can generate more highly realistic artistic stylized images than the state-of-the-art artistic style transfer methods.

Related papers

• Artist: Aesthetically Controllable Text-Driven Stylization without Training [19.5597806965592]
  We introduce textbfArtist, a training-free approach that aesthetically controls the content and style generation of a pretrained diffusion model for text-driven stylization. Our key insight is to disentangle the denoising of content and style into separate diffusion processes while sharing information between them. Our method excels at achieving aesthetic-level stylization requirements, preserving intricate details in the content image and aligning well with the style prompt.
  arXiv  Detail & Related papers  (2024-07-22T17:58:05Z)
• Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder [57.574544285878794]
  Ada-Adapter is a novel framework for few-shot style personalization of diffusion models. Our method enables efficient zero-shot style transfer utilizing a single reference image. We demonstrate the effectiveness of our approach on various artistic styles, including flat art, 3D rendering, and logo design.
  arXiv  Detail & Related papers  (2024-07-08T02:00:17Z)
• ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank [9.99530386586636]
  Artistic style transfer aims to repaint the content image with the learned artistic style. Existing artistic style transfer methods can be divided into two categories: small model-based approaches and pre-trained large-scale model-based approaches. We propose ArtBank, a novel artistic style transfer framework, to generate highly realistic stylized images.
  arXiv  Detail & Related papers  (2023-12-11T05:53:40Z)
• Style Aligned Image Generation via Shared Attention [61.121465570763085]
  We introduce StyleAligned, a technique designed to establish style alignment among a series of generated images. By employing minimal attention sharing' during the diffusion process, our method maintains style consistency across images within T2I models. Our method's evaluation across diverse styles and text prompts demonstrates high-quality and fidelity.
  arXiv  Detail & Related papers  (2023-12-04T18:55:35Z)
• ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors [105.37795139586075]
  We propose a new task for stylizing'' text-to-image models, namely text-driven stylized image generation. We present a new diffusion model (ControlStyle) via upgrading a pre-trained text-to-image model with a trainable modulation network. Experiments demonstrate the effectiveness of our ControlStyle in producing more visually pleasing and artistic results.
  arXiv  Detail & Related papers  (2023-11-09T15:50:52Z)
• Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks [0.0]
  Artistic style transfer involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs)
  arXiv  Detail & Related papers  (2023-10-27T16:21:17Z)
• InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer [22.29381866838179]
  Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. We propose a novel information disentanglement method, named InfoStyler, to capture the minimal sufficient information for both content and style representations.
  arXiv  Detail & Related papers  (2023-07-30T13:38:56Z)
• DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer [27.39248034592382]
  We propose using a new class of models to perform style transfer while enabling deformable style transfer. We show how leveraging the priors of these models can expose new artistic controls at inference time.
  arXiv  Detail & Related papers  (2023-07-09T12:13:43Z)
• Inversion-Based Style Transfer with Diffusion Models [78.93863016223858]
  Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image.
  arXiv  Detail & Related papers  (2022-11-23T18:44:25Z)
• 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)
• Interactive Style Transfer: All is Your Palette [74.06681967115594]
  We propose a drawing-like interactive style transfer (IST) method, by which users can interactively create a harmonious-style image. Our IST method can serve as a brush, dip style from anywhere, and then paint to any region of the target content image.
  arXiv  Detail & Related papers  (2022-03-25T06:38:46Z)

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.