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# coding=utf-8
# Copyright 2024 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Artwork Images - a dataset of centuries of Images prompt."""
import os
import pandas as pd
import datasets
from PIL import Image
import requests
import io
import tfds
import json
_HOMEPAGE = "https://huggingface.co./datasets/wintercoming6/artwork_for_sdxl/tree/main"
_CITATION = """\
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684-10695).
}
"""
_DESCRIPTION = """\
Artwork Images, to generate the similar artwork using stable diffusion model.
"""
_URL = "https://huggingface.co./datasets/wintercoming6/artwork_for_sdxl/resolve/main/metadata.jsonl"
_image_url = "https://huggingface.co./datasets/wintercoming6/artwork_for_sdxl/resolve/main/"
class Artwork(datasets.GeneratorBasedBuilder):
"""Artwork Images - a dataset of centuries of Images prompt."""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"prompt": datasets.Value("string"),
"image_data": datasets.Value("string"),
}
),
supervised_keys=("prompt","image_data"),
homepage=_HOMEPAGE,
)
def _split_generators(self, dl_manager):
data_files = dl_manager.download_and_extract(_URL)
df = pd.read_json(data_files, lines=True)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"files": df,
},
),
]
def download_image(self, url):
response = requests.get(url)
img = Image.open(io.BytesIO(response.content))
return img
def _generate_examples(self, files):
cnt=0
for _, row in files.iterrows():
print(cnt)
cnt+=1
print(row)
print(row.prompt)
print(type(row.prompt))
print(row.file_name)
print(type(row.file_name))
# print current os directory
p=row.prompt
n=row.file_name
print(os.getcwd())
img = self.download_image(_image_url+ row.file_name)
print(img)
examples = {}
examples["image_data"] = n
examples["prompt"] = p
# examples_json = json.dumps(examples)
print(examples)
yield examples |