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--- |
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license: apache-2.0 |
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task_categories: |
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- text-to-image |
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- image-to-image |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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# X2I Dataset |
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* Project Page: [https://vectorspacelab.github.io/OmniGen/](https://vectorspacelab.github.io/OmniGen/) |
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* Github: [https://github.com/VectorSpaceLab/OmniGen](https://github.com/VectorSpaceLab/OmniGen) |
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* Paper: [https://arxiv.org/abs/2409.11340](https://arxiv.org/abs/2409.11340) |
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* Model: [https://huggingface.co/Shitao/OmniGen-v1](https://huggingface.co/Shitao/OmniGen-v1) |
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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"**. |
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| Task| Datastet| |
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| :-------- | :-------- | |
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| Multi-modal Instruction| [X2I-mm-instruction](https://huggingface.co/datasets/yzwang/X2I-mm-instruction) | |
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| Subject-driven Editing | [X2I-subject-driven](https://huggingface.co/datasets/yzwang/X2I-subject-driven) | |
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| In-context Learning | [X2I-in-context-learning](https://huggingface.co/datasets/yzwang/X2I-in-context-learning) | |
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| Computer Vision | [X2I-computer-vision](https://huggingface.co/datasets/yzwang/X2I-computer-vision) | |
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| Text to Image Generation| [X2I-text-to-image](https://huggingface.co/datasets/yzwang/X2I-text-to-image) | |
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## X2I-in-context-learning |
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- **Derain & Enhance & GoPro** |
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A set of image derain, enhance and deblur datasets with 859 & 485 & 2,103 samples. |
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```python |
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## meta file: derain.jsonl |
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cd derain |
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tar -xzvf derain.tar.gz |
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## meta file: enhance.jsonl |
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cd enhance |
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tar -xzvf enhance.tar.gz |
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## meta file: gopro.jsonl |
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cd gopro |
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tar -xzvf gopro.tar.gz |
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``` |
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- **ADE** |
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An image segementation dataset with 297,472 samples. |
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```python |
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## meta file: ade.jsonl |
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cd ade |
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tar -xzvf ade.tar.gz |
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tar -xzvf seg_imgs.tar.gz |
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``` |
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- [MultiGen](https://github.com/salesforce/UniControl) |
