连续上下文：基于指令的图像编辑中的连续强度控制

摘要

基于指令的图像编辑提供了一种强大且直观的方式，通过自然语言来操控图像。然而，仅依赖文本指令限制了编辑程度的精细控制。我们引入了Kontinuous Kontext，这是一个指令驱动的编辑模型，它提供了对编辑强度的新维度控制，使用户能够以平滑连续的方式从无变化逐步调整至完全实现的结果。Kontinuous Kontext扩展了一个先进的图像编辑模型，使其能够接受一个额外的输入——一个标量编辑强度，该强度随后与编辑指令配对，从而实现对编辑程度的显式控制。为了注入这一标量信息，我们训练了一个轻量级的投影网络，将输入标量和编辑指令映射到模型调制空间中的系数。为了训练我们的模型，我们利用现有的生成模型合成了一组多样化的图像-编辑-指令-强度四元组数据集，随后通过过滤阶段确保质量和一致性。Kontinuous Kontext为指令驱动的编辑提供了一种统一的方法，实现了从细微到强烈的编辑强度精细控制，涵盖风格化、属性、材质、背景和形状变化等多种操作，而无需进行特定属性的训练。

English

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.

连续上下文：基于指令的图像编辑中的连续强度控制

Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing

摘要

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