Uniworld-V2：通过扩散负感知微调与MLLM隐式反馈强化图像编辑
Uniworld-V2: Reinforce Image Editing with Diffusion Negative-aware Finetuning and MLLM Implicit Feedback
AI总结：
本文提出Edit-R1框架，通过扩散负感知微调与MLLM隐式反馈优化图像编辑模型，在多个基准上取得SOTA结果。
摘要：
基于指令的图像编辑取得了显著进展；然而，仅通过监督微调训练的模型往往会过拟合标注模式，阻碍了它们在训练分布之外探索和泛化的能力。为此，我们引入了Edit-R1，这是一种基于策略优化的基于指令图像编辑的新型后训练框架。具体而言，我们采用扩散负感知微调（DiffusionNFT），这是一种与流匹配前向过程一致的无似然策略优化方法，从而能够使用高阶采样器并实现更高效的训练。此处的另一个关键挑战是缺乏通用的奖励模型，这是由编辑指令和任务的多样性导致的。为了弥合这一差距，我们采用多模态大语言模型（MLLM）作为统一的、无需训练的奖励模型，利用其输出logits提供细粒度反馈。此外，我们精心设计了一种低方差组过滤机制，以减少MLLM评分噪声并稳定优化。使用该框架训练的UniWorld-V2在ImgEdit和GEdit-Bench基准上取得了最先进的结果，分别得分4.49和7.83。至关重要的是，我们的框架是模型无关的，在应用于Qwen-Image-Edit和FLUX-Kontext等多样化的基础模型时，能带来显著的性能提升，展现了其广泛的适用性。代码和模型已公开，以支持进一步研究。
英文摘要：
Instruction-based image editing has achieved remarkable progress; however, models solely trained via supervised fine-tuning often overfit to annotated patterns, hindering their ability to explore and generalize beyond training distributions. To this end, we introduce Edit-R1, a novel post-training framework for instruction-based image editing based on policy optimization. Specifically, we utilize Diffusion Negative-aware Finetuning (DiffusionNFT), a likelihood-free policy optimization method consistent with the flow matching forward process, thereby enabling the use of higher-order samplers and more efficient training. Another key challenge here is the absence of a universal reward model, resulting from the diverse nature of editing instructions and tasks. To bridge this gap, we employ a Multimodal Large Language Model (MLLM) as a unified, training-free reward model, leveraging its output logits to provide fine-grained feedback. Furthermore, we carefully design a low-variance group filtering mechanism to reduce MLLM scoring noise and stabilize optimization. \texttt{UniWorld-V2}, trained with this framework, achieves \textbf{state-of-the-art} results on the ImgEdit and GEdit-Bench benchmarks, scoring 4.49 and 7.83, respectively. Crucially, our framework is model-agnostic, delivering substantial performance gains when applied to diverse base models like Qwen-Image-Edit and FLUX-Kontext, demonstrating its wide applicability. Code and models are publicly available to support further research.
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。
