Evaluation for stable diffusion model training
Project description
sdeval
Evaluation for stable diffusion model training
Installation
You can simply install it with pip
command line from the official PyPI site.
pip install sdeval
If your operating environment includes a available GPU, you can use the following installation command to achieve higher performance:
pip install sdeval[gpu]
For more information about installation, you can refer to Installation.
Quick Start
CCIP Metrics
from sdeval.fidelity import CCIPMetrics
ccip = CCIPMetrics(images='/path/of/character/dataset')
# ccip score of one image
print(ccip.score('/path/of/one/image'))
# ccip score of a directory of images
print(ccip.score('/directory/of/images'))
Bikini Plus Metrics
from sdeval.controllability import BikiniPlusMetrics
# build bikini plus score metrics
bp = BikiniPlusMetrics(
tag_blacklist=[
'bangs', 'long_hair', 'blue_eyes', 'animal_ears', 'sleeveless',
'breasts', 'grey_hair', 'medium_breasts'
]
)
# bp score of one image
# the image should contain a1111 webui's metadata of prompts
print(bp.score('/path/of/one/image'))
# bp score of a directory of images
# the images should contain a1111 webui's metadata of prompts
print(bp.score('/directory/of/images'))
AI Corrupt Metrics
from sdeval.corrupt import AICorruptMetrics
# build metrics
metrics = AICorruptMetrics()
# get ai corrupt score for one image file
print(metrics.score('/path/of/one/image'))
# get ai corrupt score of a directory of image files
print(metrics.score('/directory/of/images'))
# get ai corrupt score of list of images
print(metrics.score(['image1.png', 'image2.png']))
Project details
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