license: mit
SURD
By Shanshan Zhong and Zhongzhan Huang and Wushao Wen and Jinghui Qin and Liang Lin
This dataset is the implementation of "SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models" [paper][code]. Our paper has been accepted at the 31st ACM International Conference on Multimedia (ACM MM 2023, Oral).
🌻 Introduction
Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models can acquire the powerful semantic understanding and reasoning capabilities from large language models to build a high-quality textual semantic representation for text-to-image generation.
🌻 Dataset Declaration
Non-NFSW Version
As our original dataset SURD contains some sexually explicit images and others unsuitable for dissemination, we utilize nsfw toolkit to filter SURD. nsfw categorizes images into five groups: porn, hentai, sexy, neutral, and drawings (for more details, refer to description). We exclusively retain images labeled as neutral and drawings, ensuring they are safe for the workplace, thus forming the work-appropriate version of SURD (26121 samples).
Updating Dataset
You can try to collect more up-to-date data from the internet. We have provided the data scraping code for Civitai. Please take a look at processing. Afterward, prepare the dataset in the format of SURD. If you have some problems, you can try to find answers from datasets document for more details.
❣ Warning ❣: The dataset SURD proposed in our work is collected from Lexica (license), Civitai (license), and Stable Diffusion Online (license). The licenses point out that if the dataset is used for commercial purposes, there may be certain legal risks. If it is to be used for commercial purposes, please contact the relevant website or author for authorization.