Current instruction-based editing methods, such as InstructPix2Pix, often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text encoder in diffusion models. To rectify this, this paper introduces SmartEdit, a novel approach to instruction-based image editing that leverages Large Language Models (LLMs) with visual inputs to enhance their understanding and reasoning capabilities. However, direct integration of these elements still faces challenges in situations requiring complex reasoning. To mitigate this, we propose a Bidirectional Interaction Module that enables comprehensive bidirectional information interactions between the input image and the MLLM output. During training, we initially incorporate perception data to boost the perception and understanding capabilities of diffusion models. Subsequently, we demonstrate that a small amount of complex instruction editing data can effectively stimulate SmartEdit's editing capabilities for more complex instructions. We further construct a new evaluation dataset, Reason-Edit, specifically tailored for complex instruction-based image editing. Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods, paving the way for the practical application of complex instruction-based image editing.
The overall framework of SmartEdit.
Visual effects of SmartEdit on complex understanding scenarios. It can be seen that for complex understanding scenarios, SmartEdit has good instruction-based editing effects.
Visual effects of SmartEdit on complex reasoning scenarios. It can be seen that for complex reasoning scenarios, SmartEdit has good instruction-based editing effects.
The performance of SmartEdit on the MagicBrush test dataset. SmartEdit has good editing effects on the MagicBrush test dataset, not only for single-turn but also for multi-turn.
Qualitative comparison on complex understanding scenarios. Compared to other methods, SmartEdit can precisely edit specific objects in images according to instructions, while keeping the content in other areas unchanged.
Qualitative comparison on reasoning scenarios. For reasoning scenarios, SmartEdit can effectively utilize the reasoning capabilities of the LLM to identify the corresponding objects, and then edit the objects according to the instructions. Other methods perform poorly in these scenarios.
Qualitative comparison between our SmartEdit, MagicBrush-168, MagicBrush-52, InstructDiffusion, and InstructPix2Pix. Compared against other methods, SmartEdit effectively adheres to the instructions, showcasing superior results.
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