Black or White Illustrations (BoWI) is a dataset consisting of 8,192 tags-image pairs. Images are generated using Tongyi-MAI/Z-Image-Turbo at 256x512 with recommended inference parameters. Prompts passed to the model are generated using all possible tag-combinations from tags specified below. Output images are then finalized by quantizing them into absolute black or absolute white: ImageOps.grayscale(image).convert('1', dither=Image.NONE).

Example Images

Tags

TAGS = {
    "gender": [ "female", "male" ],
    "clothing_style": [ "office", "casual", "chic", "retro", "artsy", "minimal", "bohemian", "edgy" ],
    "environment": [ "beach", "city", "living room", "bedroom", "kitchen", "restaurant", "library", "classroom" ],
    "holding": [ "nothing", "water bottle", "chocolate bar", "phone", "wallet", "cup of coffee", "book", "pen" ],
    "vibe": [ "happy", "sad", "angry", "none", "casual", "professional", "annoyed", "curious" ]
}

Usually resulting in a final prompt (= a prompt passed to the model) like:

other: finished high/best quality sketch, illustration, B&W, grayscale, very sharp edges, extremely high contrast, black hair, black eyes, no/without text, pixel perfect, simple background
gender: male
clothing style: office
environment: city
holding: nothing
vibe: none

Finding images given tags is simple:

total_clothing_style = 8
total_environment    = 8
total_holding        = 8
total_vibe           = 8

# Example: (gender), (clothing_style), (environment), (holding), (vibe)
#           male,     office,           library,       nothing,   professional
gender         = 1 # male         is value 1 of the gender      key
clothing_style = 0 # office       is value 0 of the clothing    key
environment    = 6 # library      is value 6 of the environment key
holding        = 0 # nothing      is value 0 of the holding     key
vibe           = 5 # professional is value 5 of the vibe        key

index  = 0
index += gender * total_clothing_style * total_environment * total_holding * total_vibe
index += clothing_style * total_environment * total_holding * total_vibe
index += environment * total_holding * total_vibe
index += holding * total_vibe
index += vibe
# You can now use index to find {index}.png in the folder you stored the images in

Usage

1. Download Data

Obviously, to use the dataset, you need to download it. Download tags.json and images.zip to a folder where you have access to these files.
Finalize this step by unzipping images.zip, creating a folder named images with 8,192 PNG-encoded images inside. You can delete images.zip afterwards.

2. Load Tags-Image Pairs

After download the data, you can load it in any reasonable programming/scripting language you want. Here's an example using Python:

from json import loads   # standard library
from os   import listdir # standard library

IMAGE_FOLDER  = "./images/"
EXAMPLE_INDEX = 1234

BoWI = { "tags": loads(open("tags.json", "r").read().replace("'", "\"")), "images": [open(IMAGE_FOLDER+p, "rb").read() for p in listdir(IMAGE_FOLDER)] }

open(f"{EXAMPLE_INDEX}.png", "wb").write(BoWI["images"][EXAMPLE_INDEX])
print(f"Tags for the {EXAMPLE_INDEX}th index (saved the image to {EXAMPLE_INDEX}.png): {BoWI['tags'][EXAMPLE_INDEX]}")

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Contents

BoWI

Example Images

Tags

Usage

1. Download Data

2. Load Tags-Image Pairs