Datasets:
Tasks:
Image Classification
Modalities:
Image
Sub-tasks:
multi-class-image-classification
Languages:
English
License:
File size: 2,386 Bytes
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import datasets
class TTIDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description="Images generated using different TTI (Text-to-Image) models: FLUX, DreamShaper, Juggernaut XL, and Pony Diffusion V6 XL.",
features=datasets.Features({
"id": datasets.Value("int64"),
"createdAt": datasets.Value("string"),
"url": datasets.Value("string"),
"positivePrompt": datasets.Value("string"),
"negativePrompt": datasets.Value("string"),
"nsfw": datasets.Value("bool"),
"browsingLevel": datasets.Value("int32"),
"statsSummary": datasets.Value("int32"),
"usernameHash": datasets.Value("string"),
"Model": datasets.Value("string"),
"cfgScale": datasets.Value("float32"),
"sampler": datasets.Value("string"),
"Size": datasets.Value("string"),
"seed": datasets.Value("float64"),
"VAE": datasets.Value("string"),
"generationSystem": datasets.Value("string"),
"resourceIDs": datasets.Value("string"), # stored as stringified list
}),
supervised_keys=None,
homepage="",
citation="",
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract({
"flux": "flux_images.csv",
"dreamshaper": "dreamshaper_images.csv",
"juggernaut": "juggernaut_images.csv",
"pony": "pony_diffusion_images.csv"
})
return [
datasets.SplitGenerator(name="flux", gen_kwargs={"filepath": data_files["flux"]}),
datasets.SplitGenerator(name="dreamshaper", gen_kwargs={"filepath": data_files["dreamshaper"]}),
datasets.SplitGenerator(name="juggernaut", gen_kwargs={"filepath": data_files["juggernaut"]}),
datasets.SplitGenerator(name="pony", gen_kwargs={"filepath": data_files["pony"]}),
]
def _generate_examples(self, filepath):
import csv
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f)
for idx, row in enumerate(reader):
yield idx, row
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