  In this paper, we introduce GoodDrag, a novel approach to improve the
stability and image quality of drag editing. Unlike existing methods that
struggle with accumulated perturbations and often result in distortions,
GoodDrag introduces an AlDD framework that alternates between drag and
denoising operations within the diffusion process, effectively improving the
fidelity of the result. We also propose an information-preserving motion
supervision operation that maintains the original features of the starting
point for precise manipulation and artifact reduction. In addition, we
contribute to the benchmarking of drag editing by introducing a new dataset,
Drag100, and developing dedicated quality assessment metrics, Dragging Accuracy
Index and Gemini Score, utilizing Large Multimodal Models. Extensive
experiments demonstrate that the proposed GoodDrag compares favorably against
the state-of-the-art approaches both qualitatively and quantitatively. The
project page is https://gooddrag.github.io.
