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 -

MultiGen