import pandas as pd from huggingface_hub import hf_hub_url import datasets import os _VERSION = datasets.Version("0.0.1") _DESCRIPTION = "TODO" _HOMEPAGE = "TODO" _LICENSE = "TODO" _CITATION = "TODO" _FEATURES = datasets.Features( { "image": datasets.Image(), "conditioning_image": datasets.Image(), "text": datasets.Value("string"), }, ) METADATA_URL = hf_hub_url( "williamberman/fill50k", filename="train.jsonl", repo_type="dataset", ) IMAGES_URL = hf_hub_url( "williamberman/fill50k", filename="images.zip", repo_type="dataset", ) CONDITIONING_IMAGES_URL = hf_hub_url( "williamberman/fill50k", filename="conditioning_images.zip", repo_type="dataset", ) _DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION) class Fill50k(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [_DEFAULT_CONFIG] DEFAULT_CONFIG_NAME = "default" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=_FEATURES, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): metadata_path = dl_manager.download(METADATA_URL) images_dir = dl_manager.download_and_extract(IMAGES_URL) conditioning_images_dir = dl_manager.download_and_extract( CONDITIONING_IMAGES_URL ) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "metadata_path": metadata_path, "images_dir": images_dir, "conditioning_images_dir": conditioning_images_dir, }, ), ] def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir): metadata = pd.read_json(metadata_path, lines=True) for _, row in metadata.iterrows(): text = row["text"] image_path = row["image"] image_path = os.path.join(images_dir, image_path) image = open(image_path, "rb").read() conditioning_image_path = row["conditioning_image"] conditioning_image_path = os.path.join( conditioning_images_dir, row["conditioning_image"] ) conditioning_image = open(conditioning_image_path, "rb").read() yield row["image"], { "text": text, "image": { "path": image_path, "bytes": image, }, "conditioning_image": { "path": conditioning_image_path, "bytes": conditioning_image, }, }