ScaleEdit-12M: Scaling Open-Source Image Editing Data Generation via Multi-Agent Framework
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 im...
| Authors | Guanzhou Chen, Erfei Cui, Changyao Tian et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 March 2026 |
| Last updated at source | 24 March 2026 |
Models and datasets referencing this paper
- InternVL-U/ScaleEdit-12MDataset
- TTangenty/ScaleEdit-labeling-25kDataset
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