连续上下文：基于指令的图像编辑的连续强度控制
Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing
AI总结：
研究基于指令的图像编辑中编辑强度控制问题，提出连续上下文模型，通过扩展先进模型并引入标量编辑强度，训练轻量级投影网络，合成多样数据集，实现对多种图像编辑操作的编辑强度统一细粒度控制。
摘要：
基于指令的图像编辑通过自然语言提供了强大且直观的图像操作方式。然而，仅依赖文本指令限制了对编辑程度的细粒度控制。我们引入了连续上下文（Kontinuous Kontext），这是一种指令驱动的编辑模型，它提供了对编辑强度控制的新维度，能让用户以平滑连续的方式将编辑从无变化逐渐调整到完全实现的结果。连续上下文扩展了一个先进的图像编辑模型，使其接受额外输入——一个标量编辑强度，该强度与编辑指令配对，从而实现对编辑程度的明确控制。为注入此标量信息，我们训练了一个轻量级投影网络，将输入标量和编辑指令映射到模型调制空间中的系数。为训练模型，我们使用现有生成模型合成了一个包含图像 - 编辑指令 - 强度四元组的多样数据集，随后进行过滤阶段以确保质量和一致性。连续上下文为基于指令的编辑提供了一种统一方法，可在诸如风格化、属性、材质、背景和形状变化等各种操作中，从细微到强烈地对编辑强度进行细粒度控制，且无需特定属性训练。
英文摘要：
Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained control over the extent of edits. We introduce Kontinuous Kontext, an instruction-driven editing model that provides a new dimension of control over edit strength, enabling users to adjust edits gradually from no change to a fully realized result in a smooth and continuous manner. Kontinuous Kontext extends a state-of-the-art image editing model to accept an additional input, a scalar edit strength which is then paired with the edit instruction, enabling explicit control over the extent of the edit. To inject this scalar information, we train a lightweight projector network that maps the input scalar and the edit instruction to coefficients in the model's modulation space. For training our model, we synthesize a diverse dataset of image-edit-instruction-strength quadruplets using existing generative models, followed by a filtering stage to ensure quality and consistency. Kontinuous Kontext provides a unified approach for fine-grained control over edit strength for instruction driven editing from subtle to strong across diverse operations such as stylization, attribute, material, background, and shape changes, without requiring attribute-specific training.
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