metadata
license: apache-2.0
task_categories:
- text-to-image
- image-to-image
language:
- en
size_categories:
- 100K<n<1M
X2I Dataset
- Project Page: https://vectorspacelab.github.io/OmniGen/
- Github: https://github.com/VectorSpaceLab/OmniGen
- Paper: https://arxiv.org/abs/2409.11340
- Model: https://huggingface.co/Shitao/OmniGen-v1
To achieve robust multi-task processing capabilities, it is essential to train the OmniGen on large-scale and diverse datasets. However, in the field of unified image generation, a readily available dataset has yet to emerge. For this reason, we have curated a large-scale unified image generation dataset with unified format for the first time, which we refer to as the X2I dataset, meaning "anything to image".
| Task | Datastet |
|---|---|
| Multi-modal Instruction | X2I-mm-instruction |
| Subject-driven Editing | X2I-subject-driven |
| In-context Learning | X2I-in-context-learning |
| Computer Vision | X2I-computer-vision |
| Text to Image Generation | X2I-text-to-image |
X2I-in-context-learning (Few-shot to Image)
- Derain & Enhance & GoPro
A set of image derain, enhance and deblur datasets with 859 & 485 & 2,103 samples.
## meta file: derain.jsonl
cd derain
tar -xzvf derain.tar.gz
## meta file: enhance.jsonl
cd enhance
tar -xzvf enhance.tar.gz
## meta file: gopro.jsonl
cd gopro
tar -xzvf gopro.tar.gz
- ADE
An image segementation dataset with 297,472 samples.
## meta file: ade.jsonl
cd ade
tar -xzvf ade.tar.gz
cat seg_imgs.tar.gz.* | tar -xzvf -