数据集 / showlab/OmniConsistency

showlab/OmniConsistency 已完整同步

🎨 OmniConsistency: Stylized Image Pair Dataset (22 Styles)

OmniConsistency is a large-scale multi-style image translation dataset featuring 22 distinct artistic styles. Each style includes aligned image pairs:

  • src: the original image (e.g., photo or line sketch)
  • tar: the stylized image
  • prompt: a descriptive text representing the intended artistic style

This dataset is suitable for tasks such as:

  • Style transfer
  • Image-to-image generation
  • Conditional generation with prompts
  • Consistency learning

🗂️ Dataset Structure

Each style is a separate split (e.g., "Ghibli", "Pixel") in the DatasetDict format. Each sample in a split has:

{
  "src": "Ghibli/src/001.png",
  "tar": "Ghibli/tar/001.png",
  "prompt": "Ghibli Style, dreamy soft lighting, painterly landscape."
}

🔎 Note: Only image paths and prompts are provided. To access full image data, you must clone/download this repository.

🎨 Included Styles (22)

  • 3D_Chibi
  • American_Cartoon
  • Chinese_Ink
  • Clay_Toy
  • Fabric
  • Ghibli
  • Irasutoya
  • Jojo
  • LEGO
  • Line
  • Macaron
  • Oil_Painting
  • Origami
  • Paper_Cutting
  • Picasso
  • Pixel
  • Poly
  • Pop_Art
  • Rick_Morty
  • Snoopy
  • Van_Gogh
  • Vector

🧪 How to Use

Load a single style:

from datasets import load_dataset

ds = load_dataset("showlab/OmniConsistency", split="Ghibli")
print(ds[0])

Iterate through styles:

sty

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数据集版权信息

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