解决多实例图像编辑中错修、对齐不准的问题
MIRAGE: Benchmarking and Aligning Multi-Instance Image Editing
- 用视觉语言模型解析指令，分区域并行去噪实现精准定位
- 在MIRA-Bench和RefEdit-Bench上显著提升编辑精度与背景一致性
- 无需训练，适合需要高精度局部编辑的场景
指令引导的图像编辑虽在FLUX.2和Qwen-Image-Edit等模型上取得显著进展，但在包含多个相似实例及复合指令的复杂场景中仍存在严重过编辑和空间错位问题。为此，我们构建了一个专门评估多实例、多指令场景下细粒度一致性的综合基准MIRA-Bench。为应对现有方法在该基准中的失败，提出无需训练的MIRAGE框架：通过视觉语言模型将复杂指令解析为区域子集，采用多分支并行去噪策略，在注入目标区域潜在表示的同时，利用参考轨迹保持背景完整性。在MIRA-Bench和RefEdit-Bench上的大量实验表明，该框架在实现精确实例级修改的同时，显著优于现有方法，有效保持了背景一致性。相关代码与基准已开源。
原文摘要 · Abstract (English)
Instruction-guided image editing has seen remarkable progress with models like FLUX.2 and Qwen-Image-Edit, yet they still struggle with complex scenarios with multiple similar instances each requiring individual edits. We observe that state-of-the-art models suffer from severe over-editing and spatial misalignment when faced with multiple identical instances and composite instructions. To this end, we introduce a comprehensive benchmark specifically designed to evaluate fine-grained consistency in multi-instance and multi-instruction settings. To address the failures of existing methods observed in our benchmark, we propose Multi-Instance Regional Alignment via Guided Editing (MIRAGE), a training-free framework that enables precise, localized editing. By leveraging a vision-language model to parse complex instructions into regional subsets, MIRAGE employs a multi-branch parallel denoising strategy. This approach injects latent representations of target regions into the global representation space while maintaining background integrity through a reference trajectory. Extensive evaluations on MIRA-Bench and RefEdit-Bench demonstrate that our framework significantly outperforms existing methods in achieving precise instance-level modifications while preserving background consistency. Our benchmark and code are available at https://github.com/ZiqianLiu666/MIRAGE.
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