MAG-Edit: Localized Image Editing in Complex Scenarios via Mask-Based Attention-Adjusted Guidance

However, localized editing in complex the other hand, mask-free methods that utilize attention injection scenarios has not been well-studied in the literature, despite mechanisms such as Prompt-to-Prompt (P2P) [10] its growing real-world demands. Existing mask-based and Plug-and-Play (PnP) [28] can preserve the original image's inpainting methods fall short of retaining the underlying structure and layout. Nevertheless, they struggle to structure within the edit region. Meanwhile, mask-free precisely align the local editing region with the intended attention-based methods often exhibit editing leakage and text in intricate scenarios, largely due to their reliance on the misalignment in more complex compositions. In this work, text prompts' localization capabilities. As a result, editing we develop MAG-Edit, a training-free, inference-stage optimization effects often extend beyond the intended area and impact method, which enables localized image editing in incorrect regions, as shown in the fourth column of Figure 1.