UCSC-VLAA/HQ-Edit
Dataset Card for HQ-EDIT HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. HQ-Edit’s high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/HQ-Edit.
| Author / organization | UCSC-VLAA |
|---|---|
| License (as declared) | cc-by-nc-4.0 |
| Downloads | 6K (as reported by Hugging Face) |
| Likes | 42 |
| Created | 13 April 2024 |
| Last updated at source | 17 April 2024 |
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- 16 Oct 2026 Description: Dataset Card for HQ-EDIT HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data colle → Dataset Card for HQ-EDIT HQ-Edit, a high-quality instruction-based image editing dataset with total 197,350 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data colle
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