artwork_for_sdxl.py

# 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.
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"""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



_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": str,
                    "image_data": Image.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 path in files.itertuples():
            print(cnt)
            cnt+=1
            print(path)
            print(path.prompt)
            print(type(path.prompt))
            print(path.file_name)
            print(type(path.file_name))
            # print current os directory
            print(os.getcwd())
            img = self.download_image(_image_url+ path.file_name)
            print(img)
            yield {
              "prompt": path.prompt,
              "image_data": img,
            }