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.

翻译：本文提出GoodDrag，一种提升拖拽编辑稳定性和图像质量的新方法。不同于现有方法受累积扰动影响常导致变形，GoodDrag引入AlDD框架，在扩散过程中交替执行拖拽与去噪操作，有效提升结果保真度。我们还提出一种信息保持的监督运动操作，保留起始点原始特征以实现精准操控并减少伪影。此外，我们通过构建新数据集Drag100、开发专用质量评估指标（拖拽精度指数和Gemini评分，利用大型多模态模型）为拖拽编辑基准测试做出贡献。大量实验表明，所提出的GoodDrag在定性和定量结果上均优于现有最优方法。项目页面：https://gooddrag.github.io。