Birchlabs/openai-guided-diffusion-256-classcond-unguided-samples-50k

---
license: apache-2.0
pretty_name: OpenAI guided-diffusion 256px class-conditional unguided samples (50k)
size_categories:
- 10K<n<100K
---

CleanFID:
```
FID: 18.9796, KID: 0.0145976
```

Then another measurement (torchmetrics FID and CleanFID simultaneously):
```
torchmetrics FID: 19.3133
CleanFID FID: 19.1283, KID: 0.0147355
```

Read from the webdataset (after saving it somewhere on your disk) like this:

```python
from webdataset import WebDataset
from typing import TypedDict, Iterable
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)

it: Iterable[Example] = iter(dataset)
for ix, item in enumerate(it):
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')
```

原始信息汇总

数据集概述

基本信息

• 名称: OpenAI guided-diffusion 256px class-conditional unguided samples (50k)
• 许可证: Apache-2.0
• 大小类别: 10K < n < 100K

性能指标

• FID: 18.9796
• KID: 0.0145976
• torchmetrics FID: 19.3133
• CleanFID FID: 19.1283
• CleanFID KID: 0.0147355

数据加载示例

• 从webdataset加载: python from webdataset import WebDataset from typing import TypedDict, Iterable 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)

  it: Iterable[Example] = iter(dataset) for ix, item in enumerate(it): with BytesIO(item[img.png]) as stream: img: PngImageFile = Image.open(stream) img.load() img.save(f{out_root}/{ix}.png)

• 从HF dataset加载: 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)