Datasets:
Stylebreeder
Update 7/17
: We are excited to announce the release of Version 2 of our dataset, which includes 4.1 million image/prompt pairs. As part of our commitment to maintaining ethical standards, we have carefully reviewed and removed certain explicit or harmful content from the dataset. We encourage users to help us uphold these standards by reporting any concerning content via our designated Google form: https://docs.google.com/forms/d/e/1FAIpQLSeM709MtC41P-0Z-dLip-Z-hX3c72O4RQ8sL5_Rfd_fpyQLfw/viewform
Dataset Summary
Stylebreeder is a large-scale text-to-image prompt dataset. It contains 4.1 million images generated by various T2I models such as Stable Diffusion, SD-XL, and ControlNet. Images are generated by actual users from Artbreeder.
Stylebreeder is publicly available at π€ Hugging Face Dataset.
Supported Tasks and Leaderboards
The massive variety and magnitude of this AI-generated dataset presents opportunities for novel research in generative AI and computer vision.
Two Subsets
Stylebreeder provides two subsets (Stylebreeder 2M and Stylebreeder 4M) to support different needs.
Key Differences
- Stylebreeder 4M contains many more images and is a superset of Stylebreeder 2M.
- Images in Stylebreeder 2M are that of the lowest NSFW scores.
Dataset Structure
# Stylebreeder 2M
./
βββ 0000.parquet
βββ 0001.parquet
βββ 0002.parquet
βββ 0003.parquet
βββ [...]
# Stylebreeder 4M
./
βββ 0000.parquet
βββ 0001.parquet
βββ 0002.parquet
βββ 0003.parquet
βββ [...]
Data Instances
For example, below is the image of 8ecf2b5411b475b4af5cc728d295.jpeg
and its key-value pair in 0019.parquet
.

{
"f3501e05-aef7-4225-a9e9-f516527408ac.png": {
"prompt": "a landscape painting of alpine golden buckwheat and cats ear plants and valerians in a mangrove swamp, al fresco art by albert goodwin and johan messely and sanford robinson gifford and michal karcz chiaroscuro atmospheric phenomenon, crisp outlines, intricate detail 2",
"creator_id": 4329037,
"created_at": 2023-02-19 00:30:24.464962+00:00,
"modelName": stable-1.5,
"toxicity": 0.00059,
"severe_toxicity": 0,
"obscene": 0.00008,
"identity_attack": 0.0001,
"insult": 0.00015,
"threat": 0.00002,
"sexual_explicit": 0.00002,
"image_nsfw": 0,
"cluster_id": 1245
},
}
Dataset Metadata
Below are two rows from the metadata.
image_id | image_key | prompt | creator_id | created_at | modelName | toxicity | severe_toxicity | obscene | identity_attack | insult | threat | sexual_explicit | image_nsfw | cluster_id | image |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
295543512 | 699f615fa6ce37ac7aab08a629d5 | tinytipe child type steve busemi | 39096 | 2024-01-16 01:04:45.261243+00:00 | sdlx-1.0 | 0.00357 | 0.00001 | 0.00026 | 0.00025 | 0.00041 | 0.00034 | 0.00008 | 0 | 3164 | [IMG] |
276545841 | f415c6042aba5f32e654de1b71fd | wanted poster, dynamic lighting, tintype daguerrotype, solarized photo print, film grain, pastel color palette, photo by alfred stieglitz and georgia okeeffe | 39096 | 2023-04-21 05:56:45.570525+00:00 | stable-1.5 | 0.0004 | 0 | 0.00003 | 0.00009 | 0.0001 | 0.00002 | 0.00001 | 0 | 945 | [IMG] |
Metadata Schema
Column | Type | Description |
---|---|---|
image_id |
uint16 |
Image numerical ID. |
image_key |
string |
Image UUID filename. |
prompt |
string |
Text prompt used to generate this image. |
creator_id |
uint16 |
ID of user who generated this image. |
modelName |
string |
Model used to generate this image. |
toxicity |
float32 |
Toxicity score. |
severe_toxicity |
float32 |
Severe toxicity score. |
obscene |
float32 |
Obscene score. |
identity_attack |
float32 |
Identity attack score. |
insult |
float32 |
Insult score. |
threat |
float32 |
Threat score. |
sexual_explicit |
float32 |
Sexual explicit score. |
image_nsfw |
float32 |
NSFW score. |
cluster_id |
float32 |
Assigned style cluster ID. |
artists |
string |
Artists in the generated image. |
image |
float32 |
Image file. |
Method 1: Using Hugging Face Datasets Loader
The Hugging Face Datasets
library provides functions to easily load prompts and images from Stylebreeder. You can see all subsets in the Dataset Preview.
import numpy as np
from datasets import load_dataset
# Load the dataset with the `2M_sample` subset
dataset = load_dataset('stylebreeder/stylebreeder', split='2M_sample')
Method 2. Use the scraping script
import os
from datasets import load_dataset
image_folder = "./all_images"
os.makedirs(image_folder, exists_ok=True)
def download_image(image_key):
image_url = f"https://artbreeder.b-cdn.net/imgs/{image_key}.jpeg"
save_path = os.path.join(image_folder, f"{image_key}.jpeg")
response = requests.get(image_url)
if response.status_code == 200:
with open(save_path, 'wb') as file:
file.write(response.content)
else:
print(f"Failed to download image: {image_key}")
dataset = load_dataset("stylebreeder/stylebreeder", split='4M_full')
dd = dataset.select_columns(['image_key']).to_pandas()
dd.image_key.apply(lambda x: download_image(x))
Dataset Creation
Curation Rationale
As artists worldwide are increasingly leveraging text-to-image diffusion models to create artworks spanning diverse set of styles, there is a pressing need uncover and promote unique artistic expressions. This will enable users to discover unique artistic styles, generate personalized content using those styles, and get recommendations about styles they would be interested. Stylebreeder was created to address these questions, and to the best of our knowledge, has the largest userbase (95K) spanning to 4.1M images with 1.8M unique text prompts.
Collection Process
The images, prompt and other metadata including including Positive Prompt, Negative Prompt, anonymized ImageID, anonymized UserID, Timestamp, and Image Size (height, width), and model-related hyperparameters including Model Type, Seed, Step, and CFG Scale are collected from Artbreeder website. Moreover we derived additional features such as Cluster ID, Prompt NSFW, Image NSFW, Toxicity, Insult, Threat, Identity Attack scores, Artist names.
Data Instances
Each instance consists of an image generated by a text-to-image diffusion model (such as SD-XL or ControlNet) and the original text prompt used for generating the image, as well as original metadata and expanded features created by us. The metadata originally collected from the Artbreeder website are: Positive Prompt, Negative Prompt, anonymized ImageID, anonymized UserID, Timestamp, and Image Size (height, width), and model-related hyperparameters including Model Type, Seed, Step, and CFG Scale. We also offer further metadata like Cluster ID, along with scores for Prompt NSFW, Image NSFW, Toxicity, Insult, Threat, Identity Attack. We also provide potential artist names that are used in the text prompt such as βIlya Kuvshinovβ.
Who are the source producers?
The original producers of images are users of Artbreeder.
Annotations
No additional annotations.
Personal and Sensitive Information
We share anonymized user IDs and text prompts may have celebrity or artist names in the text prompts.
Considerations for Using the Data
Biases
From a societal perspective, while our tools aim to democratize art creation, there exists a risk of reinforcing existing biases present in the training data, which could skew the diversity and representation in generated artworks.
Other Limitations
One of the primary limitations lies in the potential for over-reliance on technology in artistic creation, which could diminish the value and perception of human-driven artistry and creativity. Additionally, the use of AI in art generation raises concerns about copyright and originality, especially when styles closely mimic those of existing artists without clear attribution.
Additional Information
We provide a Google form for reporting harmful or inappropriate images and prompts, as well as allowing artists to opt-out in case their names are used in the text prompts.
Licensing Information
The Stylebreeder dataset is available under the CC0 1.0 License.
Citation Information
@misc{zheng2024stylebreeder,
title={Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models},
author={Matthew Zheng and Enis Simsar and Hidir Yesiltepe and Federico Tombari and Joel Simon and Pinar Yanardag},
year={2024},
eprint={2406.14599},
archivePrefix={arXiv},
}
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