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.
Abstract (translated)
艺术风格迁移的目的是将学习到的艺术风格应用到任意内容图像上，生成艺术风格化的图像。现有的基于生成对抗网络（GAN）的方法无法生成高度逼真的艺术风格化图像，并始终引入明显的伪影和失调模式。最近，大型预训练扩散模型为生成高度逼真的艺术风格化图像开辟了新的途径。然而，扩散模型方法通常无法保留输入内容图像的內容結構，引入一些不希望的内容结构和样式模式。为了解决上述问题，我们提出了一个新颖的预训练扩散-基于的艺术风格迁移方法，称为LSAST，可以在保留输入内容图像的內容结构的同时生成高度逼真的艺术风格化图像，不会引入明显的伪影和失调样式模式。具体来说，我们引入了一个步幅感知和层感知提示空间，一系列可学习的提示，可以从艺术作品集中学习风格信息，并动态调整输入图像的內容结构和样式模式。为了训练我们的提示空间，我们提出了一个新颖的翻转方法，称为步幅感知和层感知提示翻转，允许提示空间学习艺术作品集的样式信息。此外，我们将预训练的控制网分支注入到我们的LSAST中，进一步提高了我们的框架保持内容结构的能力。大量实验证明，与最先进的艺术风格迁移方法相比，我们所提出的方法可以生成更高度逼真的艺术风格化图像。
URL
https://arxiv.org/abs/2404.11474
