Datasets:

wintercoming6
/

artwork_for_sdxl

ArXiv:

Tags:

Stable Diffusion

License:

Dataset card Files Files and versions Community

artwork_for_sdxl / artwork_for_sdxl.py

wintercoming6's picture

Update artwork_for_sdxl.py

77db138 verified 2 months ago

raw history blame contribute delete

No virus

3.26 kB

	# 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 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.Image(),
	}
	),
	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():
	# p=row.prompt
	# n=row.file_name
	# examples["image_data"] = p
	# examples["prompt"] = p
	# print(examples)
	# print(row)
	# print(row.prompt)
	# print(type(row.prompt))
	# print(row.file_name)
	# print(type(row.file_name))
	# print current os directory

	img = self.download_image(_image_url+ row.file_name)


	# examples_json = json.dumps(examples)

	yield row.file_name, {
	"image_data": img,
	"prompt": row.prompt,
	}