Kontinuous Kontext：面向指令式图像编辑的连续强度控制
Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing
把「编辑强度」做成一个连续标量条件，并通过轻量投影网络映射到扩散模型的调制空间（modulation space）系数，这为任何条件生成模型提供了一种可复用的连续控制接口；同时用现有生成模型合成 image-edit-instruction-strength 四元组再过滤，是可借鉴的弱监督数据构造流程。
原文依据 we train a lightweight projector network that maps the input scalar and the edit instruction to coefficients in the model's modulation space
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论基于指令的图像编辑通过自然语言提供了一种强大而直观的图像操控方式。然而，仅依赖文本指令限制了对编辑程度的细粒度控制。我们提出 Kontinuous Kontext，一种指令驱动的编辑模型，它提供了控制编辑强度的新维度，使用户能够以平滑且连续的方式将编辑从无变化逐步调整到完全实现的结果。Kontinuous Kontext 扩展了一个最优的图像编辑模型，使其接受一个额外的输入——标量编辑强度，该标量随后与编辑指令配对，从而实现对编辑程度的显式控制。为注入这一标量信息，我们训练了一个轻量投影网络，将输入的标量与编辑指令映射为模型调制空间中的系数。为训练该模型，我们利用现有生成模型合成了一个包含图像—编辑指令—强度四元组的多样化数据集，随后通过过滤阶段确保质量与一致性。Kontinuous Kontext 提供了一种统一的方法，可在风格化、属性、材质、背景与形状变化等多种操作上，对指令驱动编辑的强度实现从细微到强烈的细粒度控制，且无需针对特定属性进行训练。
英文原文摘要 1349 字符
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.