---
license: mit
---
# SURD
![GitHub](https://img.shields.io/github/license/gbup-group/DIANet.svg)
![GitHub](https://img.shields.io/badge/Qrange%20-group-orange)

By [Shanshan Zhong](https://github.com/zhongshsh) and [Zhongzhan Huang](https://dedekinds.github.io) and [Wushao Wen](https://scholar.google.com/citations?user=FSnLWy4AAAAJ) and [Jinghui Qin](https://github.com/QinJinghui) and [Liang Lin](https://scholar.google.com/citations?user=Nav8m8gAAAAJ&hl=en)

This dataset is the implementation of "SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models" [[paper]](https://arxiv.org/abs/2305.05189)[[code]](https://github.com/Qrange-group/SUR-adapter). 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](https://github.com/rockyzhengwu/nsfw) to filter SURD. [nsfw](https://github.com/rockyzhengwu/nsfw) categorizes images into five groups: `porn`, `hentai`, `sexy`, `neutral`, and `drawings` (for more details, refer to [description](https://github.com/alex000kim/nsfw_data_scraper?tab=readme-ov-file#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](https://civitai.com). Please take a look at [processing](https://github.com/Qrange-group/SUR-adapter/blob/main/data_collect/processing.ipynb). Afterward, prepare the dataset in the format of `SURD`. If you have some problems, you can try to find answers from [datasets document](https://huggingface.co/docs/datasets/create_dataset) for more details. 

❣ **Warning** ❣: The dataset SURD proposed in our work is collected from [Lexica](https://lexica.art) ([license](https://lexica.art/license)), [Civitai](https://civitai.com) ([license](https://github.com/civitai/civitai/blob/main/LICENSE)), and [Stable Diffusion Online](https://stablediffusionweb.com) ([license](https://huggingface.co/spaces/CompVis/stable-diffusion-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.