InstantEdit：基于分段整流流的文本引导少步图像编辑
InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow
AI总结：
InstantEdit基于RectifiedFlow框架，通过PerRFI反演、反演潜变量注入和解耦提示引导，实现快速少步文本引导图像编辑，在PIE数据集上优于现有方法。
摘要：
我们提出了一种基于RectifiedFlow框架的快速文本引导图像编辑方法，称为InstantEdit，其结构为少步编辑过程，在紧密遵循文本指令的同时保留关键内容。我们的方法利用RectifiedFlow的直线采样轨迹，引入了一种称为PerRFI的专门反演策略。为了在RectifiedFlow模型中保持一致且可编辑的结果，我们进一步提出了一种新颖的重生成方法，即反演潜变量注入，该方法有效地重用反演过程中获得的潜信息，以促进更连贯和详细的再生成。此外，我们提出了一种解耦提示引导技术，以平衡可编辑性与细节保留，并集成Canny条件ControlNet以融入结构线索并抑制伪影。在PIE图像编辑数据集上的评估表明，InstantEdit不仅速度快，而且与最先进的少步编辑方法相比，在定性和定量结果上均取得了更好的表现。
英文摘要：
We propose a fast text-guided image editing method called InstantEdit based on the RectifiedFlow framework, which is structured as a few-step editing process that preserves critical content while following closely to textual instructions. Our approach leverages the straight sampling trajectories of RectifiedFlow by introducing a specialized inversion strategy called PerRFI. To maintain consistent while editable results for RectifiedFlow model, we further propose a novel regeneration method, Inversion Latent Injection, which effectively reuses latent information obtained during inversion to facilitate more coherent and detailed regeneration. Additionally, we propose a Disentangled Prompt Guidance technique to balance editability with detail preservation, and integrate a Canny-conditioned ControlNet to incorporate structural cues and suppress artifacts. Evaluation on the PIE image editing dataset demonstrates that InstantEdit is not only fast but also achieves better qualitative and quantitative results compared to state-of-the-art few-step editing methods.
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。
