Concept Lancet：基于组合表示移植的图像编辑
Concept Lancet: Image Editing with Compositional Representation Transplant
AI总结：
提出零样本即插即用框架Concept Lancet，通过将源图像潜在表示分解为视觉概念的稀疏线性组合并执行定制概念移植，解决了扩散模型图像编辑中编辑强度难以平衡的问题，实现了最先进的编辑有效性与一致性保持。
摘要：
扩散模型被广泛用于图像编辑任务。现有编辑方法通常通过在文本嵌入或分数空间中策划编辑方向来设计表示操作流程。然而，此类流程面临一个关键挑战：高估编辑强度会损害视觉一致性，而低估则会导致编辑任务失败。值得注意的是，每张源图像可能需要不同的编辑强度，通过试错搜索合适的强度成本很高。为应对此挑战，我们提出Concept Lancet（CoLan），一个用于扩散模型图像编辑中原则性表示操作的零样本即插即用框架。在推理时，我们将潜在（文本嵌入或扩散分数）空间中的源输入分解为收集到的视觉概念表示的稀疏线性组合。这使我们能准确估计每张图像中概念的存在情况，从而为编辑提供依据。根据编辑任务（替换/添加/移除），我们执行定制的概念移植过程以施加相应的编辑方向。为充分建模概念空间，我们策划了一个概念表示数据集CoLan-150K，其中包含用于潜在字典的视觉术语和短语的多样描述与场景。在多个基于扩散的图像编辑基线上的实验表明，配备CoLan的方法在编辑有效性和一致性保持方面达到了最先进的性能。
英文摘要：
Diffusion models are widely used for image editing tasks. Existing editing methods often design a representation manipulation procedure by curating an edit direction in the text embedding or score space. However, such a procedure faces a key challenge: overestimating the edit strength harms visual consistency while underestimating it fails the editing task. Notably, each source image may require a different editing strength, and it is costly to search for an appropriate strength via trial-and-error. To address this challenge, we propose Concept Lancet (CoLan), a zero-shot plug-and-play framework for principled representation manipulation in diffusion-based image editing. At inference time, we decompose the source input in the latent (text embedding or diffusion score) space as a sparse linear combination of the representations of the collected visual concepts. This allows us to accurately estimate the presence of concepts in each image, which informs the edit. Based on the editing task (replace/add/remove), we perform a customized concept transplant process to impose the corresponding editing direction. To sufficiently model the concept space, we curate a conceptual representation dataset, CoLan-150K, which contains diverse descriptions and scenarios of visual terms and phrases for the latent dictionary. Experiments on multiple diffusion-based image editing baselines show that methods equipped with CoLan achieve state-of-the-art performance in editing effectiveness and consistency preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
