Drag-based image editing using generative models provides precise control
over image contents, enabling users to manipulate anything in an image with a
few clicks. However, prevailing methods typically adopt $n$-step iterations for
latent semantic optimization to achieve drag-based image editing, which is
time-consuming and limits practical applications. In this paper, we introduce a
novel one-step drag-based image editing method, i.e., FastDrag, to accelerate
the editing process. Central to our approach is a latent warpage function
(LWF), which simulates the behavior of a stretched material to adjust the
location of individual pixels within the latent space. This innovation achieves
one-step latent semantic optimization and hence significantly promotes editing
speeds. Meanwhile, null regions emerging after applying LWF are addressed by
our proposed bilateral nearest neighbor interpolation (BNNI) strategy. This
strategy interpolates these regions using similar features from neighboring
areas, thus enhancing semantic integrity. Additionally, a
consistency-preserving strategy is introduced to maintain the consistency
between the edited and original images by adopting semantic information from
the original image, saved as key and value pairs in self-attention module
during diffusion inversion, to guide the diffusion sampling. Our FastDrag is
validated on the DragBench dataset, demonstrating substantial improvements in
processing time over existing methods, while achieving enhanced editing
performance.

通过引入一种新的一步式拉动图像编辑方法 FastDrag，利用生成模型进行基于拖拽的图像编辑控制，通过潜在语义优化以及自我注意模块，加速编辑过程，提高编辑效果。

FastDrag: 一步操作任意物体

FastDrag: Manipulate Anything in One Step

Large language models (LLMs) have shown great success in various Natural
Language Processing (NLP) tasks, whist they still need updates after deployment
to fix errors or keep pace with the changing knowledge in the world.
Researchers formulate such problem as Model Editing and have developed various
editors focusing on different axes of editing properties. However, current
editors can hardly support all properties and rely on heavy computational
resources. In this paper, we propose a plug-in Model Editing method based on
neuron-indexed dynamic LoRA (MELO), which alters the behavior of language
models by dynamically activating certain LoRA blocks according to the index
built in an inner vector database. Our method satisfies various editing
properties with high efficiency and can be easily integrated into multiple LLM
backbones. Experimental results show that our proposed MELO achieves
state-of-the-art editing performance on three sequential editing tasks
(document classification, question answering and hallucination correction),
while requires the least trainable parameters and computational cost.

我们提出了一种基于神经元索引的动态 LoRA (MELO) 的插件模型编辑方法，通过根据内部向量数据库中的索引来动态激活特定的 LoRA 块，以改变语言模型的行为。实验结果表明，我们提出的 MELO 在文档分类、问答和幻觉修正等三个任务的编辑性能方面达到了最先进的水平，而且需要的可训练参数和计算成本最少。