ScaleEdit-12M: Scaling Open-Source Image Editing Data Generation via Multi-Agent Framework

Guanzhou Chen; Erfei Cui; Changyao Tian; Danni Yang; Ganlin Yang; Yu Qiao; Hongsheng Li; Gen Luo

cs.CVMar 21, 2026

ScaleEdit-12M: Scaling Open-Source Image Editing Data Generation via Multi-Agent Framework

Authors: Guanzhou Chen, Erfei Cui, Changyao Tian, Danni Yang, Ganlin Yang, Yu Qiao, Hongsheng Li, Gen Luo,

Organizations: Shanghai Jiao Tong University · Shanghai AI Laboratory · CUHK MMLab · Fudan University ·

Abstract

Instruction-based image editing has emerged as a key capability for unified multimodal models (UMMs), yet constructing large-scale, diverse, and high-quality editing datasets without costly proprietary APIs remains challenging. Previous image editing datasets either rely on closed-source models for annotation, which prevents cost-effective scaling, or employ fixed synthetic editing pipelines, which suffer from limited quality and generalizability. To address these challenges, we propose ScaleEditor, a fully open-source hierarchical multi-agent framework for end-to-end construction of large-scale, high-quality image editing datasets. Our pipeline consists of three key components: source image expansion with world-knowledge infusion, adaptive multi-agent editing instruction-image synthesis, and a task-aware data quality verification mechanism. Using ScaleEditor, we curate ScaleEdit-12M, the largest open-source image editing dataset to date, spanning 23 task families across diverse real and synthetic domains. Fine-tuning UniWorld-V1 and Bagel on ScaleEdit yields consistent gains, improving performance by up to 10.4% on ImgEdit and 35.1% on GEdit for general editing benchmarks and by up to 150.0% on RISE and 26.5% on KRIS-Bench for knowledge-infused benchmarks. These results demonstrate that open-source, agentic pipelines can approach commercial-grade data quality while retaining cost-effectiveness and scalability. Both the framework and dataset will be open-sourced.

Figures & tables

Figure 1: Overview of ScaleEdit. (a) Examples of instruction-based image editing in ScaleEdit, covering diverse local and global editing types. The central wheel illustrates the taxonomy of editing categories supported by ScaleEdit. (b) Size comparison between ScaleEdit and existing instruction-based image editing datasets constructed from open-source and commercial models. (c) Performance of UniWorld-V1 [ 21 ] before and after fine-tuning on ScaleEdit across multiple benchmarks (GEdit-EN-full [ 24 ] , ImgEdit-Full [ 43 ] , KRIS-Bench [ 39 ] , RISEBench [ 51 ] ), where ScaleEdit consistently brings significant improvements.

Figure 2: Overview of ScaleEditor. The framework consists of three main stages: (1) Source Image Expansion, which retrieves and synthesizes diverse images while filtering and deduplicating noisy samples; (2) Adaptive Multi-Agent Synthesis, where a task router dispatches each image to appropriate editing workflows, with specialized agents to synthesize corresponding editing instruction and images; and (3) Task-Aware Quality Verification, which assesses instruction alignment, editing consistency, and generation quality. These high-quality instruction-image-edit triplets form our ScaleEdit.

Figure 3: Diversity and quality analysis of ScaleEdit. (a) Distribution of editing subcategories demonstrates the broad coverage of editing behaviors in ScaleEdit. (b) Word cloud of editing instructions highlights the linguistic richness in edit semantics. (c) Multi-dimensional filtering score distribution across three evaluation metrics. (d) Comparison of filtering scores across representative datasets (ShareGPT-4o [ 3 ] , UltraEdit [ 50 ] , AnyEdit [ 45 ] ), where each sample's final score is defined as the minimum of its three evaluation metrics.

	Model	Background	Color	Material	Motion	Portrait	Style	Add	Remove	Replace	Text	Tone	Avg
	GPT-4o [ 27 ]	6.96	6.85	7.10	5.41	6.74	7.44	7.51	8.73	8.55	8.45	8.69	7.49
	OmniGen [ 40 ]	5.23	5.93	5.44	3.12	3.17	4.88	6.33	6.35	5.34	4.31	4.96	5.01
	Step1X-Edit [ 24 ]	7.03	6.26	6.46	3.66	5.23	7.24	7.17	6.42	7.39	7.40	6.62	6.44
Finetuning on UniWorld-V1	$\blacktriangledown$ Baseline
	UniWorld-V1 [ 21 ]	4.92	6.37	4.79	1.85	4.03	5.64	7.23	6.17	5.70	1.15	5.54	4.85
	$\blacktriangledown$ w/ Commercial Datasets

Table 1: Comparison of fine-tuning results on GEdit-EN-Full [ 24 ] . For each baseline, the best score is shown in bold .

	Model	Add	Adjust	Extract	Replace	Remove	Background	Style	Hybrid	Action	Avg
	GPT-4o [ 27 ]	4.61	4.33	2.90	4.35	3.66	4.57	4.93	3.96	4.89	4.20
	OmniGen [ 40 ]	3.47	3.04	1.71	2.94	2.43	3.21	4.19	2.24	3.38	2.96
	Step1X-Edit [ 24 ]	3.88	3.14	1.76	3.40	2.41	3.16	4.63	2.64	2.52	3.06
Finetuning on UniWorld-V1	$\blacktriangledown$ Baseline
	UniWorld-V1 [ 21 ]	3.82	3.64	2.27	3.47	3.24	2.99	4.21	2.96	2.74	3.26
	$\blacktriangledown$ w/ Commercial Datasets

Table 2: Comparison of fine-tuning results on ImgEdit-Bench [ 43 ] . For each baseline, the best score is shown in bold .

	Model	RISEBench [ 51 ]				KRIS Bench [ 39 ]
	Model	Reasoning	ApprConsistency	VisualPlausibility	Overall	Factual	Conceptual	Procedural	Overall
	GPT-4o [ 27 ]	62.80	80.20	94.90	28.90	79.80	81.37	78.32	80.09
	OmniGen [ 40 ]	22.00	32.60	55.30	0.80	33.11	28.02	23.89	28.85
	Step1X-Edit [ 24 ]	25.10	41.50	73.50	1.90	45.52	48.01	31.82	43.29
Finetuning on UniWorld-V1	$\blacktriangledown$ Baseline
	UniWorld-V1 [ 21 ]	18.33	65.79	86.63	2.22	47.71	44.80	47.92	50.27

Table 3: Comparison of fine-tuning results on RISEBench [ 51 ] and KRIS Bench [ 39 ] . For each baseline, the best score is shown in bold .

Training Data	UniWorld-V1 [ 21 ]		Janus-Pro [ 5 ]
Training Data	ImgEdit [ 43 ]	GEdit [ 24 ]	ImgEdit [ 43 ]	GEdit [ 24 ]
$\blacktriangledown$ Baseline (No Fine-tuning)
Original	3.26	4.85	–	–
$\blacktriangledown$ w/ Commercial Datasets
OpenGPT-4o-Image [ 6 ]	3.37	6.06	3.08	4.52
ShareGPT-4o-Image [ 3 ]	3.49	6.07	2.96	4.47

Table 4: Ablation study of different datasets with equal scale. All experiments are conducted under the exact same settings and data size for a fair comparison. The best score is shown in bold , and the second best is underlined .

Model	Task Router	Data Filtering	ImgEdit [ 43 ]	GEdit [ 24 ]
Janus-Pro [ 5 ]	✓	✗	3.09	4.82
	✗	✓	3.14	4.85
	✓	✓	3.17	4.92
UniWorld-V1 [ 21 ]	✓	✗	3.36	6.06
	✗	✓	3.43	6.07
	✓	✓	3.50	6.15

Table 5: Ablation study on Data Filtering and Task Router. Our routing and filtering strategy improves performance on general editing tasks across different models.

Model	Training Data	RISEBench [ 51 ]
Model	Training Data	Reasoning	Consistency	VisPlaus.	Overall
UniWorld-V1 [ 21 ]	w/o rewrite	22.75	57.35	90.26	3.05
UniWorld-V1 [ 21 ]	w/ rewrite	24.38	64.24	90.64	4.17

Table 6: Ablation study on instruction rewriting. VisPlaus. denotes the Visual Plausibility sub-dimension.

Models	Accuracy			MAE
Models	Instruction Following	Editing Consistency	Generation Quality	Instruction Following	Editing Consistency	Generation Quality
InternVL3-72B [ 52 ]	0.81	0.61	0.85	0.22	0.34	0.22
Qwen2.5-VL-72B [ 1 ]	0.82	0.63	0.89	0.17	0.28	0.17

Table 7: Alignment of open-source MLLM judges with GPT-4o. We compare the scoring consistency of different models against GPT-4o across three key quality dimensions. Higher Accuracy and lower MAE indicate better alignment with GPT-4o. Qwen2.5-VL-72B consistently outperforms InternVL3-72B across all metrics.

Models	Accuracy			MAE
Models	Instruction Following	Editing Consistency	Generation Quality	Instruction Following	Editing Consistency	Generation Quality
Qwen2.5-VL-72B [ 1 ]	0.78	0.67	0.78	0.24	0.31	0.26
GPT-4o [ 27 ]	0.86	0.75	0.82	0.16	0.22	0.20

Table 8: Alignment of MLLM judges with human preference. Higher Accuracy and lower MAE indicate better alignment with human preference. Qwen2.5-VL-72B shows highly competitive alignment with human evaluators, demonstrating its reliability at scale compared to GPT-4o.

Models	GEdit [ 24 ]		ImgEdit [ 43 ]
Models	Baseline	Finetuned	Baseline	Finetuned
InstructPix2Pix [ 2 ]	3.68	3.78	1.88	2.06
OmniGen [ 40 ]	5.06	5.49	2.96	3.15
Step1X-Edit [ 24 ]	6.70	7.14	3.06	3.21

Table 9: Finetuned results across different models. All experiments are conducted using the same settings as the equal-scale comparison.

Figure 4: Qualitative comparison of UniWorld-V1 before and after fine-tuning on ScaleEdit.

Appendix figures & tables17 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 5: Overview of the Source Image Expansion stage in ScaleEditor. Retrieval-based expansion performs image-based and text-based retrieval to collect semantically related images, while synthesis-based expansion produces high-fidelity variants, guided by detailed image captions.

Category	Editing Task	Definition
Global-level Editing	Style Transfer	Converts the overall artistic appearance of an image into a target style while preserving its structural and semantic content.
	Tone Adjustment	Adjusts global tonal parameters such as brightness, contrast, saturation, or color temperature.
	Viewpoint Transformation	Modifies the camera viewpoint or perspective geometry to present the scene from a new spatial angle.
	Background Replacement	Replaces the entire background of an image while maintaining the integrity of the foreground objects.
Object-level Editing	Object Addition	Inserts new object instances into the scene while maintaining coherent spatial relationships and lighting conditions.
	Object Removal	Removes designated objects and reconstructs the occluded background to preserve scene realism and continuity.

Appendix

Table 10: Detailed definition of editing tasks .

Dataset	Instruction Alignment			Edit Consistency			Generation Quality
Dataset	1	2	3	1	2	3	1	2	3
ScaleEdit	0.0%	0.0%	100.0%	0.0%	14.2%	85.8%	0.0%	23.5%	76.5%
ScaleEdit (pre-filtered)	7.5%	9.7%	82.8%	1.6%	14.0%	84.4%	1.2%	23.2%	75.6%
ShareGPT-4o-Image [ 3 ]	3.9%	8.3%	87.8%	0.4%	9.7%	89.9%	0.5%	12.1%	87.4%
OmniEdit [ 37 ]	5.8%	13.0%	81.2%	0.4%	24.8%	74.8%	0.5%	25.3%	74.2%
ImgEdit [ 43 ]	19.9%	25.6%	54.5%	3.7%	38.4%	58.0%	2.4%	49.2%	48.4%

Appendix

Table 11: Score distribution of each dimension across different datasets.

Dataset	(3,3,3)	(3,3,2)	(2,2,2)	(1,2,2)	(3,2,2)	(2,3,2)	(2,3,3)	(3,2,3)	(2,2,3)	(1,1,1)
ScaleEdit	85.3%	10.4%	0.0%	0.0%	2.7%	0.0%	0.0%	1.7%	0.0%	0.0%
ScaleEdit (pre-filtered)	70.6%	8.6%	4.5%	4.2%	2.2%	2.4%	1.8%	1.4%	1.1%	0.9%
ShareGPT-4o-Image [ 3 ]	81.3%	3.7%	3.3%	2.1%	1.2%	1.4%	2.5%	1.6%	1.1%	0.3%
OmniEdit [ 37 ]	63.5%	7.3%	7.6%	4.1%	4.4%	1.4%	1.9%	6.0%	2.1%	0.3%
ImgEdit [ 43 ]	37.5%	9.0%	15.3%	11.5%	6.1%	4.2%	3.8%	1.9%	2.3%	2.0%
UltraEdit [ 50 ]	44.7%	19.2%	9.1%	7.1%	11.4%	1.3%	1.0%	1.9%	0.8%	1.6%

Appendix

Table 12: The distribution of joint score combinations across datasets. Each tuple denotes (F, C, Q) .

Edit Subtask	Top-1 (%)	Top-5 (%)	Top-10 (%)	Top-20 (%)	Vocab Size
$\blacktriangledown$ Open-Ended Semantic Editing
Background Replacement	9.81	22.21	29.58	38.81	2456
Object Addition	1.48	5.53	8.86	12.50	6222
Object Removal	10.19	21.41	25.44	30.03	3987
Object Replacement	5.17	10.19	12.60	16.01	5590
Action Editing	2.54	10.12	14.61	21.09	3764

Appendix

Table 13: Target Entity Distribution. The Top-

K

columns indicate the cumulative percentage of the

K

most frequent entities. Vocab Size represents the total number of unique core entities extracted for that task. The data demonstrates a healthy long-tail distribution for open-ended tasks and extreme diversity for context-driven tasks.

Figure 6: T-SNE projection of semantic diversity across editing tasks. The absence of highly isolated or overly dense clusters demonstrates our dataset’s semantic richness and avoids repetitive or formulaic instruction generation.

Ratio	Percentage	Ratio	Percentage	Ratio	Percentage	Ratio	Percentage
0.56	0.57%	0.65	0.39%	0.66	1.36%	0.67	10.47%
0.70	0.42%	0.71	0.61%	0.80	0.77%	1.00	8.81%
1.25	0.41%	1.33	8.92%	1.34	0.44%	1.36	0.40%
1.39	0.59%	1.40	0.38%	1.49	1.13%	1.50	34.67%
1.51	3.00%	1.60	0.40%	1.78	4.56%	–	–

Appendix

Table 14: Aspect Ratio Distribution.

Parameters	UniWorld-V1 [ 21 ]	Bagel [ 9 ]
batch size	128	256
iterations	89640	44820
learning rate	$10^{-5}$	$2\times 10^{-5}$
warmup rate	0.05	0.05
optimizer	AdamW	AdamW
LR scheduler	Const	Const

Appendix

Table 15: Training Parameters for UniWorld-V1 [ 21 ] and Bagel [ 9 ] . LR scheduler denotes learning rate scheduler.

Parameters	UniWorld-V1 [ 21 ]	Janus-Pro [ 5 ]
batch size	128	32
iterations	7,812	31,250
gradient accumulation	1	4
learning rate	$10^{-5}$	$2\times 10^{-6}$
warmup rate	0.05	0.05
optimizer	AdamW	AdamW

Appendix

Table 16: Training Parameters for Ablation Studies. LR scheduler denotes learning rate scheduler.

Model	Training Dataset	RISEBench [ 51 ]				KRIS Bench [ 39 ]
Model	Training Dataset	Reasoning	ApprCons.	VisPlausi.	Overall	Factual	Conceptual	Procedural	Overall
UniWorld-V1 [ 21 ]	w/o rewrite	22.75	57.35	90.26	3.05	54.86	59.65	34.73	52.19
UniWorld-V1 [ 21 ]	w/ rewrite	24.38	64.24	90.64	4.17	56.83	63.15	35.99	54.88

Appendix

Table 17: Ablation on instruction rewriting. ApprCons. denotes Appearance Consistency sub-dimension, while VisPlausi. represents Visual Plausibility sub-dimension.

	Dataset	Background	Color	Material	Motion	Portrait	Style	Add	Remove	Replace	Text	Tone	Avg
UniWorld-V1 [ 21 ]	Pre-trained	4.92	6.37	4.79	1.85	4.03	5.64	7.23	6.17	5.70	1.15	5.54	4.85
	$\blacktriangledown$ w/ Commercial Datasets
	OpenGPT-4o-Image [ 6 ]	6.88	6.53	6.24	6.48	6.07	7.11	7.01	6.19	5.81	2.23	6.09	6.06
	ShareGPT-4o-Image [ 3 ]	6.42	7.60	6.43	6.16	6.26	6.62	7.26	5.85	5.31	2.26	6.60	6.07
	Nano-consistent [ 42 ]	6.76	6.10	4.23	5.55	6.31	5.41	6.44	1.44	5.24	1.36	6.39	5.02
	Pico-Banana [ 30 ]	6.58	7.66	5.25	6.93	6.42	5.36	6.57	6.10	6.01	2.30	6.77	5.99

Appendix

Table 18: Ablation study of different datasets with equal scale on GEdit-EN-Full [ 24 ] The best score is shown in bold , and the second best is underlined , for UniWorld-V1 and Janus-Pro, respectively.

	Dataset	Add	Adjust	Extract	Replace	Remove	Background	Style	Hybrid	Action	Avg
UniWorld-V1 [ 21 ]	Pre-trained	3.82	3.64	2.27	3.47	3.24	2.99	4.21	2.96	2.74	3.26
	$\blacktriangledown$ w/ Commercial Datasets
	OpenGPT-4o-Image [ 6 ]	4.18	3.96	1.99	3.44	2.62	3.67	4.65	2.74	3.07	3.37
	ShareGPT-4o-Image [ 3 ]	4.11	3.67	2.04	3.53	2.90	3.83	4.81	2.94	3.62	3.49
	Nano-consistent [ 42 ]	3.88	3.52	1.91	3.23	2.21	3.11	4.07	2.83	4.10	3.21
	Pico-Banana [ 30 ]	4.08	3.79	1.86	3.71	3.56	3.56	4.11	2.85	3.26	3.42

Appendix

Table 19: Ablation study of different datasets with equal scale on ImgEdit-Bench. [ 43 ] The best score is shown in bold , and the second best is underlined , for UniWorld-V1 and Janus-Pro, respectively.

Figure 7: Qualitative comparison of Bagel [ 9 ] before and after fine-tuning on ScaleEdit.

Figure 8: User interface used in the user study. The left panel displays the original image and the corresponding editing instruction, while the right panel shows four candidate edited results from different models, labeled as options A, B, C, and D. Annotators are asked to select the option that best satisfies the editing instruction.

Training Data	Top-1 preference
GPT-Image-Edit [ 35 ]	26.8%
OpenGPT-4o-Image [ 6 ]	28.1%
ImgEdit [ 43 ]	15.6%
ScaleEdit	29.5%

Appendix

Table 20: Human preference results in the user study. “Top-1 preference” denotes the percentage of tasks for which each model is selected as the best edited result.

Figure 9: Examples corresponding to each score of different metrics.

Figure 10: Examples across different editing tasks.

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Synthetic data augmentation creates or modifies training examples to improve learning when real data are scarce, imbalanced, costly, or sensitive.

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Existing image editing frameworks predominantly follow the training paradigm of text-to-image diffusion models. However, extending this paradigm to image editing highlights two inherent discrepancies, specifically, the insufficient attention to edit concept granularity and the training inefficiency caused by sparse supervision signals. To address these issues, we establish a comprehensive hierarchical taxonomy featuring over 1,000 fine-grained edit concepts and build ConceptEdit-12M, a massive dataset of 12 million high-quality editing pairs via an improved synthesis framework. This library-driven approach effectively rectifies the distribution collapse of generated data while ensuring high data fidelity. Furthermore, we propose a dense supervision training strategy that synthesizes multiple non-interfering concepts into single image pairs. By providing richer learning signals, this strategy significantly enhances both training efficiency and overall model performance. Training results validate our strategy, significantly outperforming prior works. Finally, we present ConceptEdit-Bench, a granular evaluation suite designed to diagnose model capabilities across a vast array of real-world scenarios.

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