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SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing

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arxiv 2405.04007 v1 pith:F35YA67X submitted 2024-05-07 cs.CV

Yuying Ge , Sijie Zhao , Chen Li , Yixiao Ge , Ying Shan This is my paper · ORCID
classification cs.CV
keywords editingimageseed-data-editdatadatasetmodeldatasetsdiverse
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In this technical report, we introduce SEED-Data-Edit: a unique hybrid dataset for instruction-guided image editing, which aims to facilitate image manipulation using open-form language. SEED-Data-Edit is composed of three distinct types of data: (1) High-quality editing data produced by an automated pipeline, ensuring a substantial volume of diverse image editing pairs. (2) Real-world scenario data collected from the internet, which captures the intricacies of user intentions for promoting the practical application of image editing in the real world. (3) High-precision multi-turn editing data annotated by humans, which involves multiple rounds of edits for simulating iterative editing processes. The combination of these diverse data sources makes SEED-Data-Edit a comprehensive and versatile dataset for training language-guided image editing model. We fine-tune a pretrained Multimodal Large Language Model (MLLM) that unifies comprehension and generation with SEED-Data-Edit. The instruction tuned model demonstrates promising results, indicating the potential and effectiveness of SEED-Data-Edit in advancing the field of instructional image editing. The datasets are released in https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit.

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Cited by 17 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    A diffusion-transformer framework with VLM-grounded masked attention and VAE dropout improves identity and prompt fidelity for multi-subject image generation.

  5. ADIEE: Automatic Dataset Creation and Scorer for Instruction-Guided Image Editing Evaluation

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    An automatically generated training dataset and a fine-tuned LLaVA-NeXT model produce an image editing evaluation scorer that aligns with human preference and serves as a reward model for improving editing models.

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    An instruction-guided image editor that needs no training, labels, or masks: an LLM writes before/after captions and their embedding difference guides Stable Diffusion.

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