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SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models

Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, Ying Shan

arXiv:2312.06739v1cs.CV

TL;DR

Existing instruction-based image editors often struggle with complex understanding and reasoning scenarios. SmartEdit combines an MLLM, bidirectional image-text interaction, and targeted perception and editing data, then evaluates them on Reason-Edit. The reported results show stronger performance than previous methods, although common editing metrics can be unreliable.

  • Problem

    Existing instruction-based image-editing methods often fall short on complex scenarios requiring stronger understanding and reasoning capabilities.

  • Method

    SmartEdit jointly uses an MLLM, a Bidirectional Interaction Module, perception data, and a small synthetic complex-editing dataset for instruction-based image editing.

  • Results

    Quantitative and qualitative evaluations on the 219-pair Reason-Edit dataset indicate that SmartEdit significantly outperforms previous instruction-based image-editing methods.

  • Takeaways & Limitations

    SmartEdit marks a step toward practical instruction-based image editing for complex understanding and reasoning scenarios.

  • Takeaways & Limitations

    PSNR, SSIM, LPIPS, and CLIP-Score reflect performance only to a certain extent and can rank inferior edits above better ones.

Abstract

from arXiv · show

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 Large Language Models (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.

1. Introduction

SmartEdit targets complex instruction-based image editing, where existing methods struggle with understanding, reasoning, and image-instruction integration. It combines MLLMs, bidirectional interaction, targeted training data, and the Reason-Edit benchmark to improve this setting.

  • Problem: Existing instruction-based editors handle simple instructions but often fail in complex understanding and reasoning scenarios.These include selectively modifying one object among many and identifying objects using world knowledge.
  • Motivation: Simple CLIP text encoders struggle to understand and reason about instructions and integrate image information.SmartEdit therefore introduces an MLLM and jointly optimizes it with the diffusion model.
  • Method: The Bidirectional Interaction Module enables comprehensive information exchange between image features and MLLM outputs.It addresses the unilateral modulation caused by using image features as queries and MLLM outputs as keys and values.
  • Training Strategy: Perception data and a small amount of complex instruction-editing data strengthen SmartEdit’s perception and reasoning capabilities.The strategy avoids relying solely on large amounts of expensive paired data.
  • Evaluation: Reason-Edit contains 219 image-text pairs for evaluating complex instruction-based image editing without overlap with synthesized training pairs.Both quantitative and qualitative results indicate that SmartEdit significantly outperforms previous methods.
  • Results: SmartEdit’s quantitative and qualitative results on Reason-Edit demonstrate superiority over previous instruction-based image-editing methods.The paper presents this benchmark as an evaluation of complex understanding and reasoning ability.

2. Related Work

Prior work develops diffusion-based instruction editing, broader vision-task systems, and MLLM-based vision-language models. SmartEdit builds on these directions while focusing specifically on complex instruction-based image editing.

  • Instruction-Based Editing: InstructPix2Pix uses GPT-3- and Stable-Diffusion-generated vision-language editing data to fine-tune a UNet for simple instruction-based editing.It was introduced partly because paired image-instruction editing datasets were scarce.
  • Instruction-Based Editing: MagicBrush adds a large manually annotated dataset for instruction-guided real-image editing.Its purpose is to improve editing effects on real images.
  • Unified Vision Models: InstructDiffusion jointly trains across understanding and generative vision tasks, including segmentation, keypoint detection, editing, and enhancement.SmartEdit differs by primarily focusing on instruction-based image editing, especially complex scenarios.
  • Multimodal Large Language Models: MLLM studies such as LLaVA and MiniGPT-4 improve image-text alignment through instruction tuning.The related work positions these models as a basis for leveraging language-model capabilities in vision tasks.

3. Preliminary

Instruction-based editing transforms an input image according to textual instructions using latent diffusion. SmartEdit extends this framework with an MLLM and BIM to address complex understanding and reasoning scenarios.

  • Latent Diffusion: The editing task transforms an input image x into a target image y according to instruction cT.InstructPix2Pix implements this objective using latent diffusion.
  • Latent Diffusion: The diffusion process adds noise to the encoded target-image latent, while a UNet predicts that noise from image and text conditions.The image condition is cx = E(x), and the text condition is cT.
  • Latent Diffusion: The image condition is incorporated by directly concatenating the encoded input-image latent cx with the noisy latent zt.This is the standard conditioning design described for InstructPix2Pix.
  • SmartEdit Extension: SmartEdit introduces an MLLM and BIM to provide bidirectional interaction between MLLM outputs and image information.The design targets limitations of instruction editing in complex understanding and reasoning scenarios.

4. Method

SmartEdit combines MLLM-derived instruction features with image features through bidirectional interaction before diffusion-based editing. Its training strategy adds perception data and a small synthetic complex-editing set, while Reason-Edit evaluates complex understanding and reasoning.

  • 4.1. The Framework of SmartEdit: SmartEdit processes the image and instruction with LLaVA, converts selected hidden states through QFormer, and feeds the resulting features into diffusion editing.The image feature and QFormer feature are subsequently refined by BIM before entering the diffusion model.
  • 4.1. The Framework of SmartEdit: Unlike InstructPix2Pix, SmartEdit uses refined text features as UNet keys and values and injects refined image features through residual fusion.This design changes how image and text information enter the diffusion model.
  • 4.2. Bidirectional Interaction Module: BIM performs two-way feature exchange: QFormer features attend to image features, then updated text features attend back to image features.Its design includes self-attention, two cross-attention blocks, and an MLP, producing f′ and v′.
  • 4.2. Bidirectional Interaction Module: Experiments reported in the method discussion indicate that BIM-equipped SmartEdit performs better than versions without BIM or with one-directional feature fusion.The comparison concerns bidirectional interaction against alternative fusion designs.
  • 4.3. Dataset Utilization Strategy: The training strategy adds segmentation data to improve perception and synthesizes approximately 476 complex editing pairs to stimulate reasoning capabilities.The synthetic scenarios cover object attributes such as location, color, relative size, and mirror relations.
  • 4.3. Dataset Utilization Strategy: Reason-Edit contains 219 non-overlapping image-text pairs designed to evaluate complex instruction understanding and reasoning.Its categories are consistent with those used for the synthetic training data.

5. Experiments

Experiments evaluate SmartEdit against fine-tuned instruction-editing baselines on Reason-Edit and test its BIM and dataset-training components. SmartEdit improves complex reasoning performance, while larger MLLMs and combined training data further strengthen results.

  • Comparison with State-of-the-Art Methods: SmartEdit-7B and SmartEdit-13B outperform the compared methods across five Reason-Edit metrics in reasoning scenarios.The comparison uses models fine-tuned on the same training data.
  • Comparison with State-of-the-Art Methods: SmartEdit-13B handles a qualitative reasoning example that SmartEdit-7B cannot edit accurately without changing background regions.Both models succeed on other reported reasoning examples.
  • Comparison with State-of-the-Art Methods: Ins-align improves over previous methods, and SmartEdit-13B performs better than SmartEdit-7B on this metric.PSNR, SSIM, and LPIPS do not always match the reported visual quality in understanding scenarios.
  • Ablation Study on BIM: Removing BIM causes a significant decline across all metrics in both understanding and reasoning scenarios.The ablation compares the full SmartEdit-7B model with variants that remove or alter BIM interactions.
  • Ablation Study on Dataset Usage: Segmentation data and synthetic editing data play complementary roles in improving SmartEdit’s performance.Synthetic data helps locate specific objects, while combining all datasets further improves visual effects.

6. Conclusion

The conclusion presents SmartEdit as an MLLM-enhanced instruction-editing approach for complex understanding and reasoning. BIM, combined data utilization, and Reason-Edit evaluation are identified as central components of its reported advantage.

  • Conclusion: SmartEdit incorporates visual-input language models to enhance instruction-based image-editing understanding and reasoning.The approach introduces BIM to address challenges in directly integrating language models with diffusion models.
  • Conclusion: Its data strategy combines perception data with complex instruction-editing data to improve complex-scenario handling.The conclusion describes these data types as effectively enhancing SmartEdit’s capabilities.
  • Conclusion: Evaluation on Reason-Edit shows SmartEdit outperforms previous instruction-based image-editing methods.The paper frames this result as progress toward practical complex instruction-based image editing.

Supplementary Material

The supplementary material provides additional data-pipeline details, quantitative and visual Reason-Edit comparisons, cross-method results, and training-data reasoning examples.

  • Supplementary Material: The supplement includes data-production details, additional Reason-Edit comparisons, and more visual results.It also reports results on MagicBrush and differences among SmartEdit, MGIE, and InstructDiffusion.
  • Supplementary Material: The supplement shows reasoning samples included in the synthesized training data after unsatisfied generated images were manually filtered.These samples appear in the last row of Figure 9.

7. Details of the Data Production Pipeline

SmartEdit’s synthetic training data contains complex understanding and reasoning scenarios generated through mask extraction, inpainting, image operations, and harmonization. The pipeline produces paired images and editing instructions for training.

  • Data Composition: Approximately 476 synthetic paired examples supplement conventional training data for complex understanding and reasoning scenarios.The two scenario types are complex understanding and reasoning.
  • Complex Understanding Scenarios: For complex understanding, SAM extracts object masks, MAT performs inpainting, image operations create edited compositions, and PIH harmonizes the result.The pipeline uses images x1 and x2 to replace a selected object with another object.
  • Reasoning Scenarios: For reasoning scenarios, SAM generates an object mask and Stable Diffusion performs instruction-guided inpainting.The process targets objects identified from the provided instruction.
  • Training Examples: Training examples pair input and edited images with instructions such as changing an apple to a cauliflower or a panda to a gorilla.Other examples specify spatial edits, such as adding a taller giraffe on the far left.
  • Training Examples: Figure 9 presents each synthesized example with the input image on the left and the instruction-edited image on the right.The examples cover complex understanding and reasoning scenarios.

8. More Quantitative Results on Reason-Edit

SmartEdit is evaluated quantitatively on Reason-Edit using metrics, ablations, and a user study focused on instruction alignment and image quality.

  • CLIP-Score, PSNR, SSIM, and LPIPS do not always correspond with human visual assessment of editing quality.
  • SmartEdit shows significant improvement over previous instruction-based image editing methods on complex understanding and reasoning scenarios using Ins-align.
  • The Bidirectional Interaction Module improves SmartEdit’s instruction-editing performance in complex understanding and reasoning scenarios.
  • Jointly using editing, segmentation, and synthetic editing data produces better results in complex understanding and reasoning scenarios.
  • In a 25-participant study of 30 Reason-Edit images, over 67% preferred SmartEdit for instruction alignment and more than 72% preferred its image quality.

9. More Visual Results on Reason-Edit

Visual comparisons on Reason-Edit show SmartEdit handling complex object attributes and reasoning while preserving unrelated image content better than competing methods.

  • Complex understanding: SmartEdit correctly edits specified objects across complex attributes such as location, relative size, color, and mirror relationships.
  • Complex understanding: In complex understanding examples, competing methods either produce lower-quality edits, make limited modifications, or fail to understand the instructions.
  • Complex reasoning: SmartEdit removes the fork while preserving the cake and surrounding areas, unlike methods that also modify unrelated content.
  • Complex reasoning: SmartEdit identifies the food with the most vitamins as an orange and replaces it with an apple, while other methods do not identify it.
  • Complex reasoning: Across reasoning examples, SmartEdit identifies the objects requiring edits and leaves other areas unchanged, whereas competing methods struggle with these instructions.

10. Results of SmartEdit and Other Methods on MagicBrush

On MagicBrush, SmartEdit produces effective single- and multi-turn edits and visually adheres to instructions, although standard metrics favor MagicBrush-168 and may be unreliable.

  • Qualitative results: SmartEdit shows good editing effects on MagicBrush for both single-turn and multi-turn interactions.The examples include single-turn, two-turn, and three-turn edits.
  • Quantitative comparison: MagicBrush-168 significantly outperforms SmartEdit and other methods on the reported L1, CLIP-I, CLIP-T, and DINO-I metrics.
  • Metric caveat: The L1, CLIP-I, and DINO-I metrics may not reliably reflect visual editing quality in these comparisons.
  • Qualitative comparison: SmartEdit adheres to instructions more effectively than the compared methods in qualitative MagicBrush results.

11. Difference between SmartEdit, MGIE and InstructDiffusion

SmartEdit differs from MGIE and InstructDiffusion in its emphasis on complex understanding and reasoning, its bidirectional interaction design, and its combined data strategy.

  • Difference from MGIE: SmartEdit primarily targets complex understanding and reasoning scenarios, which the authors describe as rarely discussed in MGIE.
  • Difference from MGIE: SmartEdit introduces a Bidirectional Interaction Module, distinguishing its network structure from MGIE’s approach.
  • Difference from InstructDiffusion: Unlike InstructDiffusion’s generic vision-instruction framework, SmartEdit focuses specifically on instruction-based image editing.
  • Difference from InstructDiffusion: SmartEdit combines LLaVA and BIM with segmentation and synthetic editing data to achieve satisfactory complex understanding and reasoning results.
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