instructions

README.md

@@ -1,3 +1,61 @@
 ---
 license: apache-2.0
+pretty_name: OpenAI guided-diffusion 256px class-conditional unguided samples (50k)
+size_categories:
+- 10K<n<100K
 ---
+
+Read from the webdataset (after saving it somewhere on your disk) like this:
+
+```python
+from webdataset import WebDataset
+from typing import TypedDict
+from PIL import Image
+from PIL.PngImagePlugin import PngImageFile
+from io import BytesIO
+from os import makedirs
+
+Example = TypedDict('Example', {
+  '__key__': str,
+  '__url__': str,
+  'img.png': bytes,
+})
+
+dataset = WebDataset('./openai-guided-diffusion-256-classcond-unguided-samples-50k/{00000..00004}.tar')
+
+out_root = 'out'
+makedirs(out_root, exist_ok=True)
+
+for ix, item in enumerate(dataset):
+  with BytesIO(item['img.png']) as stream:
+    img: PngImageFile = Image.open(stream)
+    img.load()
+  img.save(f'{out_root}/{ix}.png')
+```
+
+Or from the HF dataset like this:
+
+```python
+from datasets import load_dataset
+from datasets.dataset_dict import DatasetDict
+from datasets.arrow_dataset import Dataset
+from PIL.PngImagePlugin import PngImageFile
+from typing import TypedDict, Iterable
+from os import makedirs
+
+class Item(TypedDict):
+  index: int
+  tar: str
+  tar_path: str
+  img: PngImageFile
+
+dataset: DatasetDict = load_dataset('Birchlabs/openai-guided-diffusion-256-classcond-unguided-samples-50k')
+train: Dataset = dataset['train']
+
+out_root = 'out'
+makedirs(out_root, exist_ok=True)
+
+it: Iterable[Item] = iter(train)
+for item in it:
+  item['img'].save(f'{out_root}/{item["index"]}.png')
+```