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SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models (2312.06739v1)

Published 11 Dec 2023 in cs.CV

Abstract: 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 Multimodal LLMs (MLLMs) 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.

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Exploring SmartEdit: A Multimodal Approach to Complex Image Editing

The paper "SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal LLMs" introduces a pioneering approach to enhance the capabilities of instruction-based image editing using Multimodal LLMs (MLLMs). Unlike existing methods that rely solely on traditional models like CLIP, SmartEdit integrates MLLMs to better understand and execute complex image editing instructions, thereby addressing the limitations faced by existing systems in complex scenarios.

Methodological Advances and Contributions

  1. Integration of MLLMs: The paper outlines the incorporation of MLLMs to enhance the understanding and reasoning capabilities of the editing system. This is a crucial step forward from existing models that rely heavily on simplistic CLIP text encoders, which limit the capacity to handle complex, multi-object scenarios and reasoning instructions.
  2. Bidirectional Interaction Module (BIM): To facilitate effective interaction between the MLLM outputs and image features, the authors propose a Bidirectional Interaction Module. BIM ensures comprehensive bidirectional information flow, which is essential for accurately interpreting instructions and editing images in complex scenarios. This module mitigates the limitations of previous models that employed unilateral modifications by leveraging cross-attention between text and image features.
  3. Data Utilization Strategy: Recognizing the limitations posed by conventional datasets in capturing complex scenarios, SmartEdit incorporates both perception data and a synthetic dataset. This approach not only improves perception capabilities but also stimulates the reasoning capabilities of the MLLM with minimal data, providing high versatility in real-world applications.
  4. Evaluation Dataset - Reason-Edit: To effectively evaluate systems on complex instruction scenarios, the authors introduce the Reason-Edit dataset, specifically curated for evaluating understanding and reasoning abilities in instruction-based image editing tasks. This is an essential contribution for benchmarking systems like SmartEdit against its predecessors and contemporaries.

Empirical Results and Implications

SmartEdit demonstrates significant improvements over existing methods like InstructPix2Pix and InstructDiffusion, particularly in scenarios that demand a higher level of reasoning and understanding. The empirical evaluation on the Reason-Edit dataset and comparisons across multiple metrics (such as PSNR, SSIM, LPIPS, CLIP Score, and a novel Ins-align metric) indicate that SmartEdit surpasses its predecessors. These results underscore the efficacy of integrating LMMs and bespoke interaction modules to manage complex editing tasks effectively.

The implications of this research are profound, both practically and theoretically. By leveraging the strength of MLLMs, SmartEdit sets a precedent for future research in multimodal models, highlighting the potential for such systems to be employed in broader AI applications involving complex instruction comprehension and execution. Practically, SmartEdit paves the way for more intuitive and effective instruction-based image editing tools, which can be vastly beneficial in creative industries and automated design systems.

Future Prospects

As this research illuminates new pathways, it also opens several avenues for future exploration. Further studies could delve into more intricate interaction modules or the application of SmartEdit's methods across other domains such as video editing or complex scene reconstruction. Moreover, the paper's insights into data synthesis for model training could inspire innovative approaches to data generation and model ensembling, ultimately advancing the field of AI-driven content creation.

In conclusion, SmartEdit represents a sophisticated advancement in the field of image editing, building upon the strengths of MLLMs to handle tasks deemed challenging for traditional models. The integration of MLLMs and robust interaction systems and the novel dataset for evaluation position SmartEdit as a potentially transformative tool in the landscape of instruction-based AI technologies.

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Authors (11)
  1. Yuzhou Huang (5 papers)
  2. Liangbin Xie (17 papers)
  3. Xintao Wang (132 papers)
  4. Ziyang Yuan (27 papers)
  5. Xiaodong Cun (61 papers)
  6. Yixiao Ge (99 papers)
  7. Jiantao Zhou (61 papers)
  8. Chao Dong (168 papers)
  9. Rui Huang (128 papers)
  10. Ruimao Zhang (84 papers)
  11. Ying Shan (252 papers)
Citations (33)
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  1. GitHub - TencentARC/SmartEdit (170 stars)
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