样式适配器统一的风格化图像生成模型

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhouxia Wang, Xintao Wang, Liangbin Xie, Zhongang Qi, Ying Shan, Wenping Wang, Ping Luo
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引用次数: 0

摘要

这项工作的重点是生成具有特定参考图像风格和所提供文本描述内容的高质量图像。目前的主流算法,即 DreamBooth 和 LoRA,需要对每种风格进行微调,导致过程耗时且计算成本高昂。在这项工作中,我们提出了样式适配器(StyleAdapter),这是一种统一的样式化图像生成模型,能够生成与给定提示内容和参考图像样式相匹配的各种样式化图像,而无需对每种样式进行微调。它引入了双路径交叉注意(TPCA)模块,分别处理风格信息和文本提示,并与语义抑制视觉模型(SSVM)合作,抑制风格图像的语义内容。这样,既能确保提示信息保持对生成图像内容的控制,又能减轻样式参考中语义信息的负面影响。这样,生成图像的内容就会与提示保持一致,其样式也会与样式参考保持一致。此外,我们的 StyleAdapter 可以与现有的可控合成方法(如 T2I-adapter 和 ControlNet)集成,以实现更可控、更稳定的生成过程。广泛的实验证明了我们的方法优于之前的作品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

StyleAdapter: A Unified Stylized Image Generation Model

StyleAdapter: A Unified Stylized Image Generation Model

This work focuses on generating high-quality images with specific style of reference images and content of provided textual descriptions. Current leading algorithms, i.e., DreamBooth and LoRA, require fine-tuning for each style, leading to time-consuming and computationally expensive processes. In this work, we propose StyleAdapter, a unified stylized image generation model capable of producing a variety of stylized images that match both the content of a given prompt and the style of reference images, without the need for per-style fine-tuning. It introduces a two-path cross-attention (TPCA) module to separately process style information and textual prompt, which cooperate with a semantic suppressing vision model (SSVM) to suppress the semantic content of style images. In this way, it can ensure that the prompt maintains control over the content of the generated images, while also mitigating the negative impact of semantic information in style references. This results in the content of the generated image adhering to the prompt, and its style aligning with the style references. Besides, our StyleAdapter can be integrated with existing controllable synthesis methods, such as T2I-adapter and ControlNet, to attain a more controllable and stable generation process. Extensive experiments demonstrate the superiority of our method over previous works.

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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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