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---
language:
- en
license: mit
size_categories:
- 1M<n<10M
task_categories:
- image-to-image
- text-classification
- text-to-image
pretty_name: stable diffusion prompts
dataset_info:
features:
- name: image_id
dtype: int64
- name: url
dtype: string
- name: prompt
dtype: string
- name: negative_prompt
dtype: string
- name: size
dtype: string
- name: model
dtype: string
- name: stats
struct:
- name: commentCount
dtype: int64
- name: cryCount
dtype: int64
- name: dislikeCount
dtype: int64
- name: heartCount
dtype: int64
- name: laughCount
dtype: int64
- name: likeCount
dtype: int64
- name: nsfw_label
dtype: string
- name: nsfw_score
dtype: float64
splits:
- name: train
num_bytes: 877345095
num_examples: 896874
download_size: 216888972
dataset_size: 877345095
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- not-for-all-audiences
- nsfw
- uncensored
- art
- stable diffusion
---
# Dataset Card for "stable-diffusion-prompts-stats-full-uncensored"
## Not SAFE for public - Definately Unfiltered with URL links being rendered
This dataset comes from prompts shared from images' metadata on Civitai. Not for the faint of heart.
Thanks to Civitai.com for all the models, building a playground, allowing fine tuning of models, and generally being a good influence on model building and generation.
A reasonable attempt was made to tag unsafe prompts by adding a label column for 'NSFW' and 'SFW', with additional manual filtering, as well as adding a score column generated by a machine model.
The purpose of this dataset is to allow for analysis of prompts and feature analysis in prompts and negative prompts.
This could be for:
- semantic evaluation (see stats column)
- prompt quality
- effective prompting
- prompt alignment or misalignment
- statistical research on prompts and categories
- popularity of image generation approaches
- mimimalism prompts with certain models
- matching generated prompts to images for LLAVA purposes
- mimimizing prompts for better context usage
- social research on interest level and creative approaches
- modeling based on prompts for automating prompt generation strategy
- modeling of categorical interest and similarity
- modeling of evolution of prompts based on model versioning
A seperate upload includes only prompts, negative prompts, and model name for brevity, squeamishness, and research purposes. |