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GoodEnough
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TiM-Toy-T2I-Dataset

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Dataset Viewer
Split (1)
train
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
1024x1024: list<item: int64>
1024x1056: list<item: int64>
1024x1088: list<item: int64>
1024x1120: list<item: int64>
1024x1152: list<item: int64>
1024x1184: list<item: int64>
1024x1216: list<item: int64>
1024x1248: list<item: int64>
1024x1280: list<item: int64>
1024x1312: list<item: int64>
1024x1344: list<item: int64>
1024x1376: list<item: int64>
1024x1408: list<item: int64>
1024x1440: list<item: int64>
1024x1472: list<item: int64>
1024x1504: list<item: int64>
1024x1536: list<item: int64>
1024x1568: list<item: int64>
1024x1600: list<item: int64>
1024x1632: list<item: int64>
1024x1664: list<item: int64>
1024x1696: list<item: int64>
1024x1728: list<item: int64>
1024x1760: list<item: int64>
1024x1792: list<item: int64>
1024x1824: list<item: int64>
1024x1856: list<item: int64>
1024x1888: list<item: int64>
1024x1920: list<item: int64>
1024x1952: list<item: int64>
1024x1984: list<item: int64>
1024x2016: list<item: int64>
1024x2048: list<item: int64>
1024x2080: list<item: int64>
1024x2112: list<item: int64>
1024x2144: list<item: int64>
1024x2176: list<item: int64>
1024x2208: list<item: int64>
1024x2240: list<item: int64>
1024x2272: list<item: int64>
1024x2304: list<item: int64>
1024x2336: list<item: int64>
1024x2368: list<item: int64>
1024x2400: list<item: int64>
1024x2432: list<item: int64>
1024x2464: list<item: int64>
1024x2496: list<item: int64>
1024x2528: list<item: int64>
1024x2560: list<item: int64>
1024x2592: list<item: int64>
1024x2624: list<item: int64>
1024x2656: list<item: int64>
1024x2720: list<item: int64>
1024x2752: list<item: int64>
1024x2784: list<item: int64>
1024x2848: list<item: int64>
1024x2880: list<item: int64>
1024x2912: list<item: int64>
1024x2944: list<item: int64>
1024x3008: list<item: int64>
1024x3040: list<item: int64>
1024x3072: list<item: int64>
1024x3136: list<item: int64>
1024x3168: list<item: int64>
1024x3200: list<item: int64>
1024x3232: list<item: int64>
1024x3296: list<item: int64>
1024x3328: list<item: int64>
1024x3392: list<item: int64>
1024x3424: list<item: int64>
1024x3456: list<item: int64>
1024x3488: list<item: int64>
1024x3552: list<item: int64>
1024x3616: list<item: int64>
1024x3648: list<item: int64>
1024x3680: list<item: int64>
1024x3712: list<item: int64>
1024x3776: list<item: int64>
1024x3840: list<item: int64>
1024x3872: list<item: int64>
1024x3936: list<item: int64>
1024x4032: list<item: int64>
1024x4096: list<item: int64>
1024x4128: list<item: int64>
1024x4192: list<item: int64>
1024x4352: list<item: int64>
1024x4384: list<item: int64>
1024x4416: list<item: int64>
1024x4448: list<item: int64>
1024x4512: list<item: int64>
1024x4608: list<item: int64>
1024x4640: list<item: int64>
1024x4672: list<item: int64>
1024x4736: list<item: int64>
1024x4992: list<item: int64>
1024x5120: list<item: int64>
1024x5184: list<item: int64>
1024x5216: list<item: int64>
1024x5664: list<item: int64>
1024x6336: list<item: int64>
1024x6656: list<item: int64>
1024x6848: list<item: int64>
1024x6976: list<item: int64>
1024x7840: list<item: int64>
1024x9088: list<item: int64>
1024x9568: list<item: int64>
1056x1024: list<item: int64>
1088x1024: list<item: int64>
1120x1024: list<item: int64>
1152x1024: list<item: int64>
1184x1024: list<item: int64>
1216x1024: list<item: int64>
1248x1024: list<item: int64>
1280x1024: list<item: int64>
1312x1024: list<item: int64>
1344x1024: list<item: int64>
1376x1024: list<item: int64>
1408x1024: list<item: int64>
1440x1024: list<item: int64>
1472x1024: list<item: int64>
1504x1024: list<item: int64>
1536x1024: list<item: int64>
1568x1024: list<item: int64>
1600x1024: list<item: int64>
1632x1024: list<item: int64>
1664x1024: list<item: int64>
1696x1024: list<item: int64>
1728x1024: list<item: int64>
1760x1024: list<item: int64>
1824x1024: list<item: int64>
1856x1024: list<item: int64>
1920x1024: list<item: int64>
1952x1024: list<item: int64>
1984x1024: list<item: int64>
2304x1024: list<item: int64>
2368x1024: list<item: int64>
2400x1024: list<item: int64>
2432x1024: list<item: int64>
2656x1024: list<item: int64>
3104x1024: list<item: int64>
4544x1024: list<item: int64>
vs
0: int64
1: int64
2: int64
3: int64
4: int64
5: int64
6: int64
7: int64
8: int64
9: int64
10: int64
11: int64
12: int64
13: int64
14: int64
15: int64
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3422, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2187, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2391, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1882, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1904, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 543, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              1024x1024: list<item: int64>
              1024x1056: list<item: int64>
              1024x1088: list<item: int64>
              1024x1120: list<item: int64>
              1024x1152: list<item: int64>
              1024x1184: list<item: int64>
              1024x1216: list<item: int64>
              1024x1248: list<item: int64>
              1024x1280: list<item: int64>
              1024x1312: list<item: int64>
              1024x1344: list<item: int64>
              1024x1376: list<item: int64>
              1024x1408: list<item: int64>
              1024x1440: list<item: int64>
              1024x1472: list<item: int64>
              1024x1504: list<item: int64>
              1024x1536: list<item: int64>
              1024x1568: list<item: int64>
              1024x1600: list<item: int64>
              1024x1632: list<item: int64>
              1024x1664: list<item: int64>
              1024x1696: list<item: int64>
              1024x1728: list<item: int64>
              1024x1760: list<item: int64>
              1024x1792: list<item: int64>
              1024x1824: list<item: int64>
              1024x1856: list<item: int64>
              1024x1888: list<item: int64>
              1024x1920: list<item: int64>
              1024x1952: list<item: int64>
              1024x1984: list<item: int64>
              1024x2016: list<item: int64>
              1024x2048: list<item: int64>
              1024x2080: list<item: int64>
              1024x2112: list<item: int64>
              1024x2144: list<item: int64>
              1024x2176: list<item: int64>
              1024x2208: list<item: int64>
              1024x2240: list<item: int64>
              1024x2272: list<item: int64>
              1024x2304: list<item: int64>
              1024x2336: list<item: int64>
              1024x2368: list<item: int64>
              1024x2400: list<item: int64>
              1024x2432: list<item: int64>
              1024x2464: list<item: int64>
              1024x2496: list<item: int64>
              1024x2528: list<item: int64>
              1024x2560: list<item: int64>
              1024x2592: list<item: int64>
              1024x2624: list<item: int64>
              1024x2656: list<item: int64>
              1024x2720: list<item: int64>
              1024x2752: list<item: int64>
              1024x2784: list<item: int64>
              1024x2848: list<item: int64>
              1024x2880: list<item: int64>
              1024x2912: list<item: int64>
              1024x2944: list<item: int64>
              1024x3008: list<item: int64>
              1024x3040: list<item: int64>
              1024x3072: list<item: int64>
              1024x3136: list<item: int64>
              1024x3168: list<item: int64>
              1024x3200: list<item: int64>
              1024x3232: list<item: int64>
              1024x3296: list<item: int64>
              1024x3328: list<item: int64>
              1024x3392: list<item: int64>
              1024x3424: list<item: int64>
              1024x3456: list<item: int64>
              1024x3488: list<item: int64>
              1024x3552: list<item: int64>
              1024x3616: list<item: int64>
              1024x3648: list<item: int64>
              1024x3680: list<item: int64>
              1024x3712: list<item: int64>
              1024x3776: list<item: int64>
              1024x3840: list<item: int64>
              1024x3872: list<item: int64>
              1024x3936: list<item: int64>
              1024x4032: list<item: int64>
              1024x4096: list<item: int64>
              1024x4128: list<item: int64>
              1024x4192: list<item: int64>
              1024x4352: list<item: int64>
              1024x4384: list<item: int64>
              1024x4416: list<item: int64>
              1024x4448: list<item: int64>
              1024x4512: list<item: int64>
              1024x4608: list<item: int64>
              1024x4640: list<item: int64>
              1024x4672: list<item: int64>
              1024x4736: list<item: int64>
              1024x4992: list<item: int64>
              1024x5120: list<item: int64>
              1024x5184: list<item: int64>
              1024x5216: list<item: int64>
              1024x5664: list<item: int64>
              1024x6336: list<item: int64>
              1024x6656: list<item: int64>
              1024x6848: list<item: int64>
              1024x6976: list<item: int64>
              1024x7840: list<item: int64>
              1024x9088: list<item: int64>
              1024x9568: list<item: int64>
              1056x1024: list<item: int64>
              1088x1024: list<item: int64>
              1120x1024: list<item: int64>
              1152x1024: list<item: int64>
              1184x1024: list<item: int64>
              1216x1024: list<item: int64>
              1248x1024: list<item: int64>
              1280x1024: list<item: int64>
              1312x1024: list<item: int64>
              1344x1024: list<item: int64>
              1376x1024: list<item: int64>
              1408x1024: list<item: int64>
              1440x1024: list<item: int64>
              1472x1024: list<item: int64>
              1504x1024: list<item: int64>
              1536x1024: list<item: int64>
              1568x1024: list<item: int64>
              1600x1024: list<item: int64>
              1632x1024: list<item: int64>
              1664x1024: list<item: int64>
              1696x1024: list<item: int64>
              1728x1024: list<item: int64>
              1760x1024: list<item: int64>
              1824x1024: list<item: int64>
              1856x1024: list<item: int64>
              1920x1024: list<item: int64>
              1952x1024: list<item: int64>
              1984x1024: list<item: int64>
              2304x1024: list<item: int64>
              2368x1024: list<item: int64>
              2400x1024: list<item: int64>
              2432x1024: list<item: int64>
              2656x1024: list<item: int64>
              3104x1024: list<item: int64>
              4544x1024: list<item: int64>
              vs
              0: int64
              1: int64
              2: int64
              3: int64
              4: int64
              5: int64
              6: int64
              7: int64
              8: int64
              9: int64
              10: int64
              11: int64
              12: int64
              13: int64
              14: int64
              15: int64
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YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)
arxiv.org/abs/2509.04394
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TiM

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Transition Models • 4 items • Updated 1 day ago