Unlocking the Potential of Image Editing via Concept Scaling and Dense Supervision
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 hi...
| Authors | Long Cui, Xiaoqian Liu, Qi Qin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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