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About this paper
Yuying Ge, Sijie Zhao, Chen Li, Yixiao Ge, Ying Shan
Tencent AI Lab developed SEED-Data-Edit, a comprehensive hybrid dataset of 3.7 million image editing pairs and 21,000 multi-turn sequences, designed to advance instruction-guided image editing. A model fine-tuned on this dataset, SEED-X-Edit, demonstrated improved instruction adherence, detail preservation, and realistic results across various editing types compared to existing baselines.
Key insights
- A hybrid data generation approach, combining automated pipelines with real-world scenarios and human expertise, creates a comprehensive and high-quality dataset for instruction-guided image editing.
- The inclusion of multi-turn editing sequences, sourced from professional workflows, enables the training of models capable of iterative and context-aware image manipulation.
- Leveraging multimodal large language models (MLLMs) and advanced computer vision techniques for data generation allows for large-scale, diverse, and precisely annotated editing pairs covering various modification types.
Summary by alphaXiv.
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