NumeriKontrol: 为基于指令的图像编辑添加数字控制
NumeriKontrol: Adding Numeric Control to Diffusion Transformers for Instruction-based Image Editing
AI总结：
NumeriKontrol通过引入数字控制框架，实现基于指令的图像编辑中对编辑强度的精确控制，提升交互编辑的灵活性和精度。
摘要：
基于指令的图像编辑通过自然语言命令实现直观操控。然而，单独的文本指令往往缺乏对编辑强度进行精细控制所需的精度。我们引入了NumeriKontrol框架，允许用户通过连续的标量值和常见单位精确调整图像属性。NumeriKontrol通过有效的Numeric Adapter编码数字编辑尺度，并以即插即用的方式将它们注入扩散模型中。得益于任务分离的设计，我们的方法支持零样本多条件编辑，允许用户以任意顺序指定多个指令。为了提供高质量的监督，我们从可靠的来源合成精确的训练数据，包括高保真渲染引擎和DSLR相机。我们的Common Attribute Transform（CAT）数据集涵盖了多样化的属性操控，并具有准确的地面真实尺度，使NumeriKontrol能够作为简单而强大的交互编辑工作室发挥作用。大量实验表明，NumeriKontrol在广泛的属性编辑场景中能够提供准确、连续和稳定的尺度控制。这些贡献通过使图像操控更加精确、可扩展和用户可控，推动了基于指令的图像编辑的发展。
英文摘要：
Instruction-based image editing enables intuitive manipulation through natural language commands. However, text instructions alone often lack the precision required for fine-grained control over edit intensity. We introduce NumeriKontrol, a framework that allows users to precisely adjust image attributes using continuous scalar values with common units. NumeriKontrol encodes numeric editing scales via an effective Numeric Adapter and injects them into diffusion models in a plug-and-play manner. Thanks to a task-separated design, our approach supports zero-shot multi-condition editing, allowing users to specify multiple instructions in any order. To provide high-quality supervision, we synthesize precise training data from reliable sources, including high-fidelity rendering engines and DSLR cameras. Our Common Attribute Transform (CAT) dataset covers diverse attribute manipulations with accurate ground-truth scales, enabling NumeriKontrol to function as a simple yet powerful interactive editing studio. Extensive experiments show that NumeriKontrol delivers accurate, continuous, and stable scale control across a wide range of attribute editing scenarios. These contributions advance instruction-based image editing by enabling precise, scalable, and user-controllable image manipulation.
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