Datasets:

quchenyuan
/

text-to-art-database

pretty_name: Vieutopia T2A Privacy Train v1
language:
  - en
license: other
size_categories:
  - 100K<n<1M
task_categories:
  - text-to-image
task_ids:
  - text-to-image-generation
annotations_creators:
  - machine-generated
source_datasets:
  - original
tags:
  - text-to-image
  - image-generation
  - diffusion
  - privacy-preserving
  - synthetic
configs:
  - config_name: samples
    data_files:
      - split: train
        path: data/samples/train/*.parquet
      - split: validation
        path: data/samples/validation/*.parquet
      - split: test
        path: data/samples/test/*.parquet
  - config_name: iterations
    data_files:
      - split: train
        path: data/iterations/train/*.parquet
      - split: validation
        path: data/iterations/validation/*.parquet
      - split: test
        path: data/iterations/test/*.parquet

Vieutopia T2A Privacy Train v1

Dataset Summary

Privacy-safe text-to-image dataset repacked into Parquet shards with embedded image bytes.

Scope: text-to-image outputs only
Excluded: image-to-image pipelines (pix2pix_*, pst_*)
Privacy: no raw task UUIDs, no user/device fields
Storage format: parquet shards (image as binary bytes), no image_path dependency

Splits

samples

train: 117572
validation: 6532
test: 6532
total: 130636

iterations

train: 477440
validation: 52256
test: 52256
total: 581952

Schema

samples config fields

sample_id (string)
task_id_hash (string)
prompt (string)
negative_prompt (string)
width (int32)
height (int32)
has_iteration (bool)
iteration_count (int32)
file_sha256 (string)
split (string)
created_date (string)
image (binary image bytes)

iterations config fields

sample_id (string)
frame_idx (int32)
split (string)
image (binary image bytes)

Loading Example

from datasets import load_dataset, Image

samples = load_dataset("quchenyuan/text-to-art-database", "samples")
samples = samples.cast_column("image", Image())

iterations = load_dataset("quchenyuan/text-to-art-database", "iterations")
iterations = iterations.cast_column("image", Image())