

Ultraedit
Overview :
UltraEdit is a large-scale image editing dataset comprising approximately 4 million automatically generated, instruction-based image editing samples. It leverages the creativity of large language models (LLMs) and the contextual editing examples provided by human evaluators, offering a systematic approach to produce large-scale and high-quality image editing samples. Key advantages of UltraEdit include:
1) **Wider Range of Editing Instructions:** It utilizes the creativity of LLMs and contextual editing examples from human evaluators to provide a broader spectrum of editing instructions.
2) **Diverse Data Source:** Its data source is based on real-world images, encompassing photographs and artwork, leading to increased diversity and reduced bias.
3) **Region-Based Editing Support:** Enhanced by high-quality, automatically generated region annotations, it supports region-based editing.
Target Users :
UltraEdit dataset caters to researchers and developers in the field of image editing, particularly those focused on instruction-based image editing techniques. It offers a rich resource for developing and training advanced image editing models, ultimately enhancing their ability to understand and execute complex editing tasks.
Use Cases
Add a UFO to the sky
Add a moon to the sky
Add cherry blossoms
Dress her in a short purple wedding dress adorned with white floral embroidery
Put a tribal chief's headdress on her
Features
Provides large-scale, high-quality image editing samples
Utilizes large language models and human evaluators' contextual editing examples
Based on real-world image data source, increasing diversity and reducing bias
Supports region-based editing, enhanced by automatically generated region annotations
Sets new records in MagicBrush and Emu-Edit benchmark tests
Confirms the importance of real-image anchor points and region-based editing data through experiments and analysis
How to Use
1. Visit the UltraEdit official website to access the dataset.
2. Select appropriate image editing samples based on research or development needs.
3. Utilize the dataset samples to train or test image editing models.
4. Conduct qualitative assessments of the editing results generated by the model.
5. Evaluate model performance using benchmarks like MagicBrush or Emu-Edit.
6. Optimize model parameters and algorithms based on the assessment results.
7. Apply the trained model to real-world image editing tasks.
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