連續上下文：基於指令的圖像編輯之連續強度控制

摘要

基於指令的圖像編輯提供了一種強大且直觀的方式，通過自然語言來操控圖像。然而，僅依賴文本指令限制了對編輯程度的精細控制。我們引入了Kontinuous Kontext，這是一個指令驅動的編輯模型，它提供了一種新的控制維度——編輯強度，使用戶能夠以平滑連續的方式，從無變化逐步調整至完全實現的結果。Kontinuous Kontext擴展了一種先進的圖像編輯模型，使其能夠接受一個額外的輸入，即一個標量編輯強度，該強度隨後與編輯指令配對，從而實現對編輯範圍的顯式控制。為了注入這一標量信息，我們訓練了一個輕量級的投影網絡，該網絡將輸入的標量與編輯指令映射到模型調製空間中的係數。為了訓練我們的模型，我們利用現有的生成模型合成了一個多樣化的圖像-編輯-指令-強度四元組數據集，並通過過濾階段確保質量和一致性。Kontinuous Kontext提供了一種統一的方法，用於在指令驅動的編輯中實現從細微到強烈的精細控制，涵蓋風格化、屬性、材質、背景和形狀變化等多樣化操作，而無需進行特定屬性的訓練。

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.

連續上下文：基於指令的圖像編輯之連續強度控制

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

摘要

Support