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MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks

Mingsong Li, Lin Liu, Hongjun Wang, Haoxing Chen, Xijun Gu, Shizhan Liu, Dong Gong, Junbo Zhao, Zhenzhong Lan, Jianguo Li

arXiv:2509.14638v1cs.CV

TL;DR

Existing instruction-based image editing datasets offer limited coverage of challenging edits, and caption-based construction can lose or misalign visual information. MultiEdit provides a large, diverse dataset built with image-grounded instructions and high-fidelity edited targets. Fine-tuning with its training set improves sophisticated editing while preserving standard-benchmark performance.

  • Problem

    Existing datasets focus mainly on simple natural-image edits, while caption-based construction can lose visual information and misalign instructions.

    Source: §1 · pp. 1–2

  • Method

    MultiEdit combines over 107K editing samples with an MLLM-driven pipeline for visual-adaptive instructions and high-fidelity edited images.

    Source: §1 · p. 2

  • Results

    Fine-tuning foundational models with MultiEdit-Train improves sophisticated editing while maintaining performance on the standard editing benchmark.

    Source: §1 · p. 3, §6 · p. 14

  • Takeaways & Limitations

    MultiEdit provides a resource for exploring more diverse and challenging instruction-based image-editing capabilities.

    Source: §6 · p. 14

    As a supplementary dataset, MultiEdit requires broader foundational corpora to train a general-purpose IBIE model from scratch effectively.

    Source: Limitations · p. 14

Abstract

from arXiv · show

Current instruction-based image editing (IBIE) methods struggle with challenging editing tasks, as both editing types and sample counts of existing datasets are limited. Moreover, traditional dataset construction often contains noisy image-caption pairs, which may introduce biases and limit model capabilities in complex editing scenarios. To address these limitations, we introduce MultiEdit, a comprehensive dataset featuring over 107K high-quality image editing samples. It encompasses 6 challenging editing tasks through a diverse collection of 18 non-style-transfer editing types and 38 style transfer operations, covering a spectrum from sophisticated style transfer to complex semantic operations like person reference editing and in-image text editing. We employ a novel dataset construction pipeline that utilizes two multi-modal large language models (MLLMs) to generate visual-adaptive editing instructions and produce high-fidelity edited images, respectively. Extensive experiments demonstrate that fine-tuning foundational open-source models with our MultiEdit-Train set substantially improves models' performance on sophisticated editing tasks in our proposed MultiEdit-Test benchmark, while effectively preserving their capabilities on the standard editing benchmark. We believe MultiEdit provides a valuable resource for advancing research into more diverse and challenging IBIE capabilities. Our dataset is available at https://huggingface.co/datasets/inclusionAI/MultiEdit.

1 Introduction

Existing IBIE datasets concentrate on simpler natural-image edits, while caption-based construction can lose visual information and misalign instructions. MultiEdit addresses this gap with a broad dataset, image-grounded instruction and edit generation, and evaluation showing gains on sophisticated edits while preserving standard-benchmark performance.

1 Introduction

  • Existing datasets mainly cover local or global single-entity edits, leaving spatial or semantic reasoning and structured-image manipulations underrepresented.Source: §1 · p. 1
  • Caption-rephrasing pipelines inherit information loss, noise, and misalignment, which can yield low-quality data or costly correction.Source: §1 · p. 2
  • The pipeline uses an MLLM to derive visual-adaptive instructions directly from source images and ImageGen to generate high-fidelity edited targets.Source: §1 · pp. 2–3
  • MultiEdit contains 107,634 samples across six tasks and 56 editing types, split into 106,534 training and 1,100 test examples.Source: §1 · pp. 2–3, Table 1 · p. 3
  • Fine-tuning foundational models with MultiEdit-Train substantially improves sophisticated editing while maintaining performance on the standard editing benchmark.Source: §1 · p. 3

2 MultiEdit

MultiEdit organizes instruction-based editing into six task categories and 56 editing types, with a pipeline for selecting source images, generating adaptive instructions and edits, and assessing quality. It also addresses task imbalance through multi-task training strategies.

2.1 Editing Type Definition

  • MultiEdit covers object and person reference editing, text and GUI editing, view editing, and style transfer across six task categories.The task definitions specify the targeted attributes and source-image domains for these edit types.Source: §2.1 · p. 4

2.2 Dataset Collection

  • The pipeline selects real-world source images, generates visual-adaptive instructions and edited images, and assesses instruction-image alignment, edit fidelity, and preservation.Non-style instructions are generated directly from images; style transfer uses templates, and output-review procedures vary with an initial 70% accuracy threshold.Source: §2.2 · p. 5

2.3 Characteristics and Statistics

  • MultiEdit contains over 107K fine-grained image-instruction-edit triplets, including 55,297 style-transfer samples across 38 styles.The style distribution emphasizes popular styles while maintaining coverage across three artistic categories.Source: §2.3 · p. 6

2.4 Multi-task Learning Framework

  • MultiEdit’s uneven task difficulty and sample counts motivate multi-task training with staged task-group sampling and sample-wise loss reweighting.The explored reweighting strategies use group inverse frequency with smoothing or per-sample gradient norms.Source: §2.4 · pp. 6–8

3 Experiments

The experiments compare open-source editing models on MultiEdit-Test and EmuEdit-Test, then evaluate fine-tuning foundational models with MultiEdit data. Fine-tuning improves complex-task performance, and qualitative comparisons show both gains and remaining weaknesses.

3.1 Experimental Setup

  • The evaluation compares SD3, UltraEdit, AnyEdit, and Step1X-Edit using semantic, visual, and pixel-level metrics across MultiEdit-Test and EmuEdit-Test.Models are fine-tuned for five epochs; SD3 and UltraEdit use SD3-based models, with only MMDiT activated during fine-tuning.Source: §3.1 · p. 8, §3.2 · pp. 8–9

3.2 Quantitative Evaluation

  • SD3 fine-tuning raises MultiEdit-Test CLIPimg and DINO by approximately 9.4% and 16.1%, while mixed-data training reaches EmuEdit-Test scores of 0.8319 CLIPimg and 0.7551 DINO.UltraEdit fine-tuning raises MultiEdit-Test CLIPimg by approximately 1.1% and DINO by 7.2%; task-weighted sampling further improves its results while preserving EmuEdit-Test capabilities.Source: Table 2 · p. 9, §3.2 · p. 9

3.3 Qualitative Evaluation

  • Fine-tuning improves instruction comprehension and editing precision, but facial fidelity remains a weakness; Step1X-Edit struggles with reference edits, and AnyEdit with style transfer.The qualitative comparison covers object reference, person reference, text, GUI, view, and style-transfer scenarios.Source: §3.3 · p. 9

4 Discussion

The discussion tests data- and loss-level multi-task strategies, then examines mixing MultiEdit training data with UltraEdit data. Task-weighted sampling is the strongest reported strategy, while mixed data brings larger gains on EmuEdit-Test than on MultiEdit-Test.

4.1 Multi-task Optimization Strategies

  • Setting4 task-weighted sampling gives the best overall results on MultiEdit-Test and EmuEdit-Test, supporting stronger weights for the more complex G3 and G4 tasks.The authors adopt Setting4 as the standard data-driven multi-task setup for their primary experiments.Source: §4.1 · p. 11, Table 3 · p. 12

4.2 Mixed Data Training

  • Mixing UltraEdit data produces marginal MultiEdit-Test gains but larger EmuEdit-Test gains, peaking at 0.8409 CLIPimg and 0.7668 DINO.The authors select the 100K mixing configuration for their main method comparisons.Source: Table 4 · p. 12, §4.2 · p. 13

4.1 Multi-task Optimization Strategies

  • Within loss-driven strategies, inverse-proportional gradient balancing performs best, but task-weighted data sampling yields higher gains on MultiEdit-Test.For intra-batch task-group balancing, the unsmoothed version slightly outperforms the smoothed version.Source: §4.1 · p. 12

5 Related Work

Prior work advances instruction-based editing models and expands datasets in scale and generation fidelity, but complex task coverage remains a concern. MultiEdit adds a dataset organized around diverse, challenging editing tasks.

Instruction-based Image Editing Models

  • Instruction-based editing models pursue instruction fidelity, multimodal interaction, training efficiency, or integration of multimodal reasoning with controllable generation.Examples include SmartEdit, UltraEdit, and AnyEdit for interaction and fidelity; ICEdit for training efficiency; and Step1X-Edit and Qwen-Image for multimodal reasoning with diffusion-based generation.Source: §5 · p. 13

Instruction-based Image Editing Datasets

  • Existing editing datasets trade off scale, access, fidelity, and task breadth, while complex compositional and detail-oriented editing remains under-covered.Emu Edit is described as a 10-million-pair proprietary dataset; HQ-Edit has 197K pairs without pixel-to-pixel alignment; UltraEdit has 4 million pairs; and AnyEdit has 2.5 million pairs spanning varied tasks.Source: §5 · p. 13

The SOTA ImageGen-related Synthetic Datasets

  • MultiEdit targets diverse, challenging editing with six categories, 18 non-style-transfer types, and 38 styles grouped across three style-transfer categories.The related-work discussion situates it among corpora generated using SOTA ImageGen, including OmniConsistency, Echo-4o-Image, ShareGPT-4o-Image, and GPT-Image-Edit-1.5M.Source: §5 · p. 13

6 Conclusion

MultiEdit provides a large, diverse dataset and a construction pipeline for challenging instruction-based image editing, with experiments showing improved sophisticated editing while preserving standard-benchmark performance.

6 Conclusion

  • MultiEdit contains over 107K samples across six challenging tasks, with 18 non-style-transfer editing types and 38 style variations.The pipeline uses an MLLM for visual-adaptive instruction generation and ImageGen for high-fidelity edited image generation.Source: §6 · p. 13
  • Fine-tuning SD3-based foundational models with MultiEdit-Train substantially improves sophisticated editing while effectively preserving performance on the standard benchmark.The experiments also explore multi-task learning strategies.Source: §6 · p. 14
  • The dataset is partitioned into an approximately 106K-sample training set and a curated 1.1K-sample evaluation benchmark.The authors present MultiEdit as a resource for exploring more diverse and challenging editing capabilities.Source: §6 · p. 14

Limitations

The dataset is supplementary rather than sufficient for training a general-purpose IBIE model from scratch, and experiments are constrained by the current SD-based architecture.

Limitations

  • MultiEdit requires integration with broader foundational corpora for effective from-scratch training, while the current SD-based architecture constrains its experimental potential.The authors identify next-generation models such as FLUX and unified multimodal architectures as future directions.Source: Limitations · p. 14

A Text Editing Related to Chinese Characters

ImageGen has difficulty producing reliable Chinese text edits, especially for longer text and images with substantial pre-existing Chinese advertising text; these cases are excluded from the final dataset.

A Text Editing Related to Chinese Characters

  • ImageGen translates short English phrases into Chinese with reasonable performance, but longer Chinese text reduces stability and detail preservation, and edits to existing Chinese advertising text often contain corruptions.The Auto-poster text subset and Chinese Translation editing type are excluded from the final dataset.Source: A Text Editing Related to Chinese… · p. 17

B Systematic Expert-designed Meta-instructions

The pipeline uses expert-designed meta-instructions to generate visual-adaptive editing instructions, with templates for style transfer and task-specific prompts for other complex edits. The prompts generally require precise target descriptions and direct instructions within 50 tokens.

Instruction-generation strategy

  • Expert-designed meta-instructions structure visual-adaptive instruction generation, using templates for style transfer and task-specific suites for other complex tasks.The pipeline employs a state-of-the-art MLLM to generate an instruction from each source image.Source: B Systematic Expert-designed… · p. 17

Person edits

  • Person-edit prompts target a randomly selected person for changes to pose, clothing, hairstyle, skin color, or figure, with the person’s position specified precisely.Each prompt requests a direct instruction within 50 tokens.Source: B Systematic Expert-designed… · p. 18

Document edits

  • Document-edit prompts cover font style, expression, font color, and display medium, specifying the selected text’s location where applicable.The prompts request direct instructions of no more than 50 tokens and caution against selecting text that is too small.Source: B Systematic Expert-designed… · pp. 18–19

Icon and object edits

  • Icon and object prompts request changes to icon attributes or to an object’s color, shape, scale, or position, with the target’s location described precisely.The icon and object instructions are requested directly and kept within 50 tokens.Source: B Systematic Expert-designed… · pp. 18–19

Alternative views

  • Alternative-view prompts cover people, landmarks, and objects, requiring the new perspective to retain some overlap with the original image.The requested instructions are direct and limited to 50 tokens.Source: B Systematic Expert-designed… · p. 19

C Model Inference Guidance Experiments

The inference setup uses 50 steps for most models and model-specific guidance settings. UltraEdit and its fine-tuned variants use a shared text guidance scale, with image guidance varying by model and benchmark.

C Model Inference Guidance Experiments

  • 50 inference steps are used for all models except AnyEdit, which uses its default 100 steps.Baseline guidance settings are retained; AnyEdit uses image and text guidance scales of 3.0, Step1X-Edit uses cfg guidance 6.0, and SD3 uses strength 0.6 and guidance 7.0.Source: C Model Inference Guidance Experiments · p. 19
  • A text guidance scale of 4.0 is used for UltraEdit and its fine-tuned models.UltraEdit uses image guidance 2.0; UltraEdit-based fine-tuned models use 3.5 on MultiEdit-Test and 5.0 on EmuEdit-Test, while SD3-based variants use 3.5 and 2.0, respectively.Source: C Model Inference Guidance Experiments · p. 19
  • Guidance-scale tests on MultiEdit-Test and EmuEdit-Test are summarized in Table 5, with best results marked per group.Table 6 summarizes inference hyperparameters for all methods.Source: Table 5 · p. 20, Table 6 · p. 20

D Detailed Quantitative Results on MultiEdit-Test

ME-UEdit-DMTL, the best-performing method fine-tuned on MultiEdit-Train, consistently excels across several challenging MultiEdit-Test tasks. The comparisons also describe scenario-specific limitations in baseline methods.

D Detailed Quantitative Results on MultiEdit-Test

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