InstructUDrag: Joint Text Instructions and Object Dragging for Interactive Image Editing
This addresses the problem of flexible and precise interactive image editing for users of text-to-image models, representing an incremental improvement by integrating existing techniques.
The paper tackles the limitations of text-based and object-dragging methods in image editing by proposing InstructUDrag, a diffusion-based framework that combines text instructions with object dragging, enabling simultaneous precise object relocation and semantic control, with experiments showing high-fidelity results.
Text-to-image diffusion models have shown great potential for image editing, with techniques such as text-based and object-dragging methods emerging as key approaches. However, each of these methods has inherent limitations: text-based methods struggle with precise object positioning, while object dragging methods are confined to static relocation. To address these issues, we propose InstructUDrag, a diffusion-based framework that combines text instructions with object dragging, enabling simultaneous object dragging and text-based image editing. Our framework treats object dragging as an image reconstruction process, divided into two synergistic branches. The moving-reconstruction branch utilizes energy-based gradient guidance to move objects accurately, refining cross-attention maps to enhance relocation precision. The text-driven editing branch shares gradient signals with the reconstruction branch, ensuring consistent transformations and allowing fine-grained control over object attributes. We also employ DDPM inversion and inject prior information into noise maps to preserve the structure of moved objects. Extensive experiments demonstrate that InstructUDrag facilitates flexible, high-fidelity image editing, offering both precision in object relocation and semantic control over image content.