Language has emerged as a natural interface for image editing. In this paper,
we introduce a method for region-based image editing driven by textual prompts,
without the need for user-provided masks or sketches. Specifically, our
approach leverages an existing pretrained text-to-image model and introduces a
bounding box generator to find the edit regions that are aligned with the
textual prompts. We show that this simple approach enables flexible editing
that is compatible with current image generation models, and is able to handle
complex prompts featuring multiple objects, complex sentences or long
paragraphs. We conduct an extensive user study to compare our method against
state-of-the-art methods. Experiments demonstrate the competitive performance
of our method in manipulating images with high fidelity and realism that align
with the language descriptions provided. Our project webpage:
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这篇论文介绍了一种基于文本提示的区域图像编辑方法，无需用户提供的掩码或草图，通过利用现有的预训练文本到图像模型和引入边界框生成器来找到与文本提示对齐的编辑区域，实现了与当前图像生成模型兼容的灵活的编辑，并处理了包含多个对象、复杂句子或长段落的复杂提示。通过广泛的用户研究实验证明我们方法在操纵图像时与提供的语言描述相一致，具有高保真度和逼真度的竞争性表现。