StyleBooth：基于多模态指令的图像风格编辑
StyleBooth: Image Style Editing with Multimodal Instruction
AI总结：
StyleBooth提出统一文本与图像指令的扩散模型框架，并构建高质量风格编辑数据集，实现文本、示例及组合式风格编辑，实验证明数据质量显著提升内容保持与生成质量。
摘要：
给定一张原始图像，图像编辑旨在生成与所提供的指令相符的图像。其挑战在于接受多模态输入作为指令，以及高质量训练数据的稀缺性，这些数据包括关键的源/目标图像对和多模态（文本和图像）指令的三元组。在本文中，我们专注于图像风格编辑，并提出了StyleBooth，一种为图像编辑提供全面框架以及构建高质量风格编辑数据集的可行策略的方法。我们将编码后的文本指令和图像示例整合为扩散模型的统一条件，从而能够根据多模态指令对原始图像进行编辑。此外，通过迭代的风格-去风格调整与编辑以及可用性过滤，StyleBooth数据集提供了各种风格类别中内容一致的风格化/普通图像对。为了展示StyleBooth的灵活性，我们在多种任务上进行了实验，如基于文本的风格编辑、基于示例的风格编辑和组合式风格编辑。结果表明，训练数据的质量和多样性显著增强了在编辑任务中保留内容的能力，并提高了生成图像的整体质量。项目页面可在https://ali-vilab.github.io/stylebooth-page/找到。
英文摘要：
Given an original image, image editing aims to generate an image that align with the provided instruction. The challenges are to accept multimodal inputs as instructions and a scarcity of high-quality training data, including crucial triplets of source/target image pairs and multimodal (text and image) instructions. In this paper, we focus on image style editing and present StyleBooth, a method that proposes a comprehensive framework for image editing and a feasible strategy for building a high-quality style editing dataset. We integrate encoded textual instruction and image exemplar as a unified condition for diffusion model, enabling the editing of original image following multimodal instructions. Furthermore, by iterative style-destyle tuning and editing and usability filtering, the StyleBooth dataset provides content-consistent stylized/plain image pairs in various categories of styles. To show the flexibility of StyleBooth, we conduct experiments on diverse tasks, such as text-based style editing, exemplar-based style editing and compositional style editing. The results demonstrate that the quality and variety of training data significantly enhance the ability to preserve content and improve the overall quality of generated images in editing tasks. Project page can be found at https://ali-vilab.github.io/stylebooth-page/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
