ReasonEdit: 向推理增强的图像编辑模型迈进
ReasonEdit: Towards Reasoning-Enhanced Image Editing Models
AI总结：
ReasonEdit通过引入思考和反思机制，提升图像编辑模型的推理能力，实现更准确的编辑效果。
摘要：
最近图像编辑模型的进步显示出显著的进展。一种常见的架构设计将多模态大语言模型（MLLM）编码器与扩散解码器相结合，如Step1X-Edit和Qwen-Image-Edit系统所示，其中MLLM编码参考图像和指令，但在训练过程中保持冻结。在本工作中，我们证明解锁MLLM的推理能力可以进一步推动编辑模型的边界。具体来说，我们探索了两种推理机制，思考和反思，这些机制增强了指令理解和编辑准确性。基于此，我们提出的框架使图像编辑能够在思考-编辑-反思循环中进行：思考机制利用MLLM的世界知识来解释抽象指令，而反思则审查编辑结果，自动纠正无意的修改，并识别停止轮次。广泛的实验表明，我们的推理方法取得了显著的性能提升，当从Step1X-Edit（ReasonEdit-S）初始化我们的DiT时，在ImgEdit（+4.3%）、GEdit（+4.7%）和Kris（+8.2%）上实现了改进，并且在与Qwen-Image-Edit（ReasonEdit-Q）结合时，也优于之前的开源方法。
英文摘要：
Recent advances in image editing models have shown remarkable progress. A common architectural design couples a multimodal large language model (MLLM) encoder with a diffusion decoder, as seen in systems such as Step1X-Edit and Qwen-Image-Edit, where the MLLM encodes both the reference image and the instruction but remains frozen during training. In this work, we demonstrate that unlocking the reasoning capabilities of MLLM can further push the boundaries of editing models. Specifically, we explore two reasoning mechanisms, thinking and reflection, which enhance instruction understanding and editing accuracy. Based on that, our proposed framework enables image editing in a thinking-editing-reflection loop: the thinking mechanism leverages the world knowledge of MLLM to interpret abstract instructions, while the reflection reviews editing results, automatically corrects unintended manipulations, and identifies the stopping round. Extensive experiments demonstrate that our reasoning approach achieves significant performance gains, with improvements of ImgEdit (+4.3%), GEdit (+4.7%), and Kris (+8.2%) when initializing our DiT from the Step1X-Edit (ReasonEdit-S), and also outperforms previous open-source methods on both GEdit and Kris when integrated with Qwen-Image-Edit (ReasonEdit-Q).
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。
