FlashEdit: 解耦速度、结构和语义以实现精确图像编辑
FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing
AI总结：
本文提出FlashEdit，一种高效的局部图像编辑框架，通过解耦速度、结构和语义来实现精确编辑，实验表明其在保真度和效率之间取得了良好的平衡。
摘要：
基于文本的图像编辑使用扩散模型已取得了显著的高质量成果，但往往面临可接受的延迟问题。我们介绍了FlashEdit，一种针对标准反向编辑设置的实时局部图像编辑框架。其效率和精度源于三个关键创新：（1）一个循环一致的一步反向（COSI）管道，通过循环一致性鼓励流形对齐的一步反向；（2）一种背景屏蔽（BG-Shield）技术，通过结构自注意干预提高非编辑区域的保真度；（3）一种稀疏的空间交叉注意（SSCA）机制，通过抑制语义泄漏促进精确编辑。在PIE-Bench上的实验表明，FlashEdit在保真度和效率之间取得了良好的权衡，编辑可在0.2秒内完成，比基于DDIM的多步编辑快超过150倍。我们的代码将在https://github.com/JunyiWuCode/FlashEdit上公开发布。
英文摘要：
Text-guided image editing with diffusion models has achieved remarkable quality but often suffers from prohibitive latency. We introduce \textbf{FlashEdit}, a real-time localized image editing framework for the standard inversion-based editing setting. Its efficiency and precision stem from three key innovations: (1) a \textbf{Cycle-Consistent One-Step Inversion (COSI)} pipeline that encourages manifold-aligned one-step inversion through cycle consistency; (2) a \textbf{Background Shield (BG-Shield)} technique that improves preservation of non-edited regions via structural self-attention intervention; and (3) a \textbf{Sparsified Spatial Cross-Attention (SSCA)} mechanism that promotes precise edits by suppressing semantic leakage. Experiments on PIE-Bench demonstrate a strong preservation-efficiency trade-off, with edits completed in under 0.2 seconds and an over 150$\times$ speedup over DDIM-based multi-step editing. Our code will be made publicly available at \url{https://github.com/JunyiWuCode/FlashEdit}.
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。
