vera365/lexica_dataset

LexicaDataset

LexicaDataset is a large-scale text-to-image prompt dataset shared in [USENIX'24] Prompt Stealing Attacks Against Text-to-Image Generation Models. It contains 61,467 prompt-image pairs collected from Lexica. All prompts are curated by real users and images are generated by Stable Diffusion. Data collection details can be found in the paper.

Data Splits

We randomly sample 80% of a dataset as the training dataset and the rest 20% as the testing dataset.

Load LexicaDataset

You can use the Hugging Face Datasets library to easily load prompts and images from LexicaDataset.

import numpy as np
from datasets import load_dataset

trainset = load_dataset('vera365/lexica_dataset', split='train')
testset  = load_dataset('vera365/lexica_dataset', split='test')

Metadata Schema

trainset and testset share the same schema.

Column	Type	Description
image	image	The generated image
prompt	string	The text prompt used to generate this image
id	string	Image UUID
promptid	string	Prompt UUID
width	uint16	Image width
height	uint16	Image height
seed	uint32	Random seed used to generate this image.
grid	bool	Whether the image is composed of multiple smaller images arranged in a grid
model	string	Model used to generate the image
nsfw	string	Whether the image is NSFW
subject	string	the subject/object depicted in the image, extracted from the prompt
modifier10	sequence	Modifiers in the prompt that appear more than 10 times in the whole dataset. We regard them as labels to train the modifier detector
modifier10_vector	sequence	One-hot vector of modifier10

Ethics & Disclosure

According to the terms and conditions of Lexica, images on the website are available under the Creative Commons Noncommercial 4.0 Attribution International License. We strictly followed Lexica’s Terms and Conditions, utilized only the official Lexica API for data retrieval, and disclosed our research to Lexica. We also responsibly disclosed our findings to related prompt marketplaces.

License

The LexicaDataset dataset is available under the CC-BY 4.0 License.

Citation

If you find this useful in your research, please consider citing:

@inproceedings{SQBZ24,
  author = {Xinyue Shen and Yiting Qu and Michael Backes and Yang Zhang},
  title = {{Prompt Stealing Attacks Against Text-to-Image Generation Models}},
  booktitle = {{USENIX Security Symposium (USENIX Security)}},
  publisher = {USENIX},
  year = {2024}
}