README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: created_at
    dtype: string
  - name: prompt
    dtype: string
  - name: negative_prompt
    dtype: string
  - name: likes
    dtype: int64
  - name: sampler
    dtype: string
  - name: height
    dtype: int64
  - name: steps
    dtype: int64
  - name: width
    dtype: int64
  - name: cursor
    dtype: int64
  - name: url
    dtype: string
  - name: cfg_scale
    dtype: float64
  - name: model
    dtype: string
  splits:
  - name: train
    num_bytes: 248764537
    num_examples: 256224
  download_size: 54319285
  dataset_size: 248764537
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- image generation
- negative prompts
- stable-diffusion
pretty_name: NegOpt Full
language:
- en
size_categories:
- 100K<n<1M
task_categories:
- text-to-image
- text-generation
---

This is the dataset constructed in and used to fine-tune the models proposed in our paper [Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation](https://arxiv.org/abs/2403.07605). The data is gotten from [Playground](https://playground.com/).

If you find this dataset useful, please cite us here:
```
@article{ogezi2024optimizing,
  title={Optimizing Negative Prompts for Enhanced Aesthetics and Fidelity in Text-To-Image Generation},
  author={Ogezi, Michael and Shi, Ning},
  journal={arXiv preprint arXiv:2403.07605},
  year={2024}
}
```