UniEdit-I：通过迭代理解、编辑与验证实现统一VLM的免训练图像编辑
UniEdit-I: Training-free Image Editing for Unified VLM via Iterative Understanding, Editing and Verifying
提出在统一VLM的语义潜空间中构建理解-编辑-验证闭环，利用VLM的多模态推理能力实时评估中间结果并动态调整编辑强度，为图像编辑提供免训练、自校正的新范式。
原文依据 We propose UniEdit-I, the first training-free, closed-loop image editing framework that operates entirely within the semantic latent space of a unified VLM
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论扩散模型和视觉语言模型（VLM）的最新进展显著提升了图像编辑的可控性。FlowEdit等方法能够沿着可见的无噪声轨迹进行逐步编辑，其中每个中间结果都是清晰的图像，无需完整的噪声反演。然而，这些方法仍在像素空间或变分自编码器潜空间中操作，中间输出往往存在视觉伪影、失真或不真实的细节，导致可靠的语义评估变得困难。此外，它们仍是开环系统，应用静态编辑而没有反馈来指导或自适应地纠正编辑过程。我们提出UniEdit-I，这是首个完全在统一VLM的语义潜空间中运行的免训练闭环图像编辑框架，通过引入理解-编辑-验证（UEV）循环：（1）理解：将源图像和编辑指令解析为结构化的源提示词和最小目标规范；（2）编辑：应用动态语义偏移，并采用可配置的反馈加权机制，根据实时对齐反馈自适应地调节编辑强度；（3）验证：利用VLM自身的多模态推理能力，沿多个语义维度评估中间输出，并触发提前停止或细化。通过将VLM从事后评估器转变为过程指挥者，UniEdit-I建立了首个语义驱动、自校正的闭环图像编辑流程。在GEdit-Bench上评估，UniEdit-I在无需任何微调或架构修改的情况下达到了最优性能，甚至超越了几个大规模预训练编辑器。
英文原文摘要 1764 字符
Recent advances in diffusion models and vision-language models (VLMs) have significantly enhanced the controllability of image editing. Methods like FlowEdit enable step-by-step editing along a visible, noise-free trajectory, where each intermediate result is a clear image, eliminating the need for full noise inversion. However, these approaches still operate in pixel space or VAE latent space, where intermediate outputs often suffer from visual artifacts, distortions, or unrealistic details--making reliable semantic evaluation difficult. Furthermore, they remain open-loop systems, applying static edits without feedback to guide or correct the editing process adaptively. We propose UniEdit-I, the first training-free, closed-loop image editing framework that operates entirely within the semantic latent space of a unified VLM by introducing an Understanding-Editing-Verifying (UEV) loop:(1) Understanding: parses the source image and editing instruction into a structured source prompt and a minimal target specification;(2) Editing: applies dynamic semantic offsets, with a configurable feedback weighting mechanism that adaptively modulates editing intensity based on real-time alignment feedback;(3) Verifying: leverages the VLM's own multimodal reasoning capability to evaluate the intermediate output along multiple semantic dimensions and trigger early stopping or refinement. By transforming the VLM from a post-hoc evaluator into an in-process conductor, UniEdit-I establishes the first semantics-driven, self-correcting closed-loop image editing pipeline. Evaluated on GEdit-Bench, UniEdit-I achieves state-of-the-art performance without any fine-tuning or architectural modifications, and even surpasses several large-scale pre-trained editors.