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
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介绍了 GoodDrag，一种改善拖拽编辑稳定性和图像质量的新方法。通过引入 AlDD 框架进行扩散过程中的拖拽和去噪操作交替，有效提高结果的保真度。提出了信息保持的运动监督操作，以保持精确操作和减少伪影。除此之外，通过引入 Drag100 数据集和开发专用的质量评估指标，Dragging Accuracy Index 和 Gemini Score 来贡献拖拽编辑的基准测试。广泛的实验证明，所提出的 GoodDrag 在定性和定量上与最先进的方法相比具有优势。