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instructions

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  ---
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  license: apache-2.0
 
 
 
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  license: apache-2.0
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+ pretty_name: OpenAI guided-diffusion 256px class-conditional unguided samples (20 samples)
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+ size_categories:
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+ - n<1K
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  ---
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+
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+ Read from the webdataset (after saving it somewhere on your disk) like this:
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+
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+ ```python
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+ from webdataset import WebDataset
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+ from typing import TypedDict
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+ from PIL import Image
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+ from PIL.PngImagePlugin import PngImageFile
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+ from io import BytesIO
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+ from os import makedirs
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+
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+ Example = TypedDict('Example', {
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+ '__key__': str,
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+ '__url__': str,
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+ 'img.png': bytes,
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+ })
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+
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+ dataset = WebDataset('./wds-dataset-viewer-test/{00000..00001}.tar')
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+
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+ out_root = 'out'
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+ makedirs(out_root, exist_ok=True)
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+
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+ for ix, item in enumerate(dataset):
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+ with BytesIO(item['img.png']) as stream:
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+ img: PngImageFile = Image.open(stream)
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+ img.load()
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+ img.save(f'{out_root}/{ix}.png')
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+ ```
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+
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+ Or from the HF dataset like this:
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+
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+ ```python
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+ from datasets import load_dataset
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+ from datasets.dataset_dict import DatasetDict
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+ from datasets.arrow_dataset import Dataset
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+ from PIL.PngImagePlugin import PngImageFile
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+ from typing import TypedDict, Iterable
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+ from os import makedirs
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+
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+ class Item(TypedDict):
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+ index: int
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+ tar: str
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+ tar_path: str
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+ img: PngImageFile
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+
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+ dataset: DatasetDict = load_dataset('Birchlabs/wds-dataset-viewer-test')
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+ train: Dataset = dataset['train']
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+
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+ out_root = 'out'
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+ makedirs(out_root, exist_ok=True)
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+
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+ it: Iterable[Item] = iter(train)
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+ for item in it:
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+ item['img'].save(f'{out_root}/{item["index"]}.png')
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+ ```