|
|
--- |
|
|
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/](https://vectorspacelab.github.io/OmniGen/) |
|
|
* Github: [https://github.com/VectorSpaceLab/OmniGen](https://github.com/VectorSpaceLab/OmniGen) |
|
|
* Paper: [https://arxiv.org/abs/2409.11340](https://arxiv.org/abs/2409.11340) |
|
|
* Model: [https://huggingface.co/Shitao/OmniGen-v1](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](https://huggingface.co/datasets/yzwang/X2I-mm-instruction) | |
|
|
| Subject-driven Editing | [X2I-subject-driven](https://huggingface.co/datasets/yzwang/X2I-subject-driven) | |
|
|
| In-context Learning | [X2I-in-context-learning](https://huggingface.co/datasets/yzwang/X2I-in-context-learning) | |
|
|
| Computer Vision | [X2I-computer-vision](https://huggingface.co/datasets/yzwang/X2I-computer-vision) | |
|
|
| Text to Image Generation| [X2I-text-to-image](https://huggingface.co/datasets/yzwang/X2I-text-to-image) | |
|
|
|
|
|
## X2I-computer-vision |
|
|
|
|
|
- **ADE** |
|
|
|
|
|
A image segementation dataset with 297,472 samples. |
|
|
|
|
|
```python |
|
|
## meta file: ade.jsonl |
|
|
cd ade |
|
|
tar -xzvf ade.tar.gz |
|
|
``` |
|
|
|
|
|
Download all tar parts (ade/seg_imgs.tar.gz.*) from [here](https://huggingface.co/datasets/yzwang/X2I-in-context-learning/tree/main/ade) |
|
|
```python |
|
|
cd ade |
|
|
cat seg_imgs.tar.gz.* | tar -xzvf - |
|
|
``` |
|
|
|
|
|
- **Rain100H** |
|
|
|
|
|
A image derain dataset with 100 samples. |
|
|
|
|
|
```python |
|
|
## meta file: derain_rain100h.jsonl |
|
|
cd derain_rain100h |
|
|
tar -xzvf derain_rain100h.tar.gz |
|
|
``` |
|
|
|
|
|
- **LOL** |
|
|
|
|
|
A low-light image enhancement dataset with 485 samples. |
|
|
|
|
|
```python |
|
|
## meta file: enhance_lol.jsonl |
|
|
cd enhance_lol |
|
|
tar -xzvf enhance_lol.tar.gz |
|
|
``` |
|
|
|
|
|
- **MuLan** |
|
|
|
|
|
A multi-layer image editing dataset with 32,066 samples. |
|
|
|
|
|
```python |
|
|
## meta file: mulan.jsonl |
|
|
cd mulan |
|
|
tar -xzvf mulan.tar.gz |
|
|
|
|
|
cd coco2017 |
|
|
tar -xzvf coco2017.tar.gz |
|
|
``` |
|
|
|
|
|
- **ReasonSeg** |
|
|
|
|
|
A image reasoning segmentation dataset with 1,218 samples. |
|
|
|
|
|
```python |
|
|
## meta file: reasonseg.jsonl |
|
|
cd reasonseg |
|
|
tar -xzvf reasonseg.tar.gz |
|
|
``` |
|
|
|
|
|
- **WeatherStream & GoPro** |
|
|
|
|
|
A image derain, desnow and deblur dataset with 3,386 samples. |
|
|
|
|
|
```python |
|
|
## meta file: weather-stream_gopro.jsonl |
|
|
cd weather-stream/rain |
|
|
tar -zxvf rain.tar.gz |
|
|
|
|
|
cd weather-stream/snow |
|
|
tar -zxvf snow.tar.gz |
|
|
|
|
|
cd gopro |
|
|
tar -zxvf gopro.tar.gz |
|
|
|
|
|
cd raindrop |
|
|
tar -zxvf raindrop.tar.gz |
|
|
``` |
|
|
|
|
|
- [MultiGen](https://github.com/salesforce/UniControl) |
