Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
identity: struct<settings: struct<model: string, repo_id: string, pipeline: string, num_inference_steps: int64 (... 281 chars omitted)
child 0, settings: struct<model: string, repo_id: string, pipeline: string, num_inference_steps: int64, guidance_scale: (... 209 chars omitted)
child 0, model: string
child 1, repo_id: string
child 2, pipeline: string
child 3, num_inference_steps: int64
child 4, guidance_scale: double
child 5, max_sequence_length: int64
child 6, seed: int64
child 7, generator_device: string
child 8, width: int64
child 9, height: int64
child 10, dtype: string
child 11, quantization: null
child 12, attention: string
child 13, model_revision: string
child 14, resolution_source: string
child 1, prompts_sha256: string
child 2, case_ids: list<item: string>
child 0, item: string
python: string
slurm_job_id: string
cuda_visible_devices: string
versions: struct<torch: string, diffusers: string, transformers: string, accelerate: string>
child 0, torch: string
child 1, diffusers: string
child 2, transformers: string
child 3, accelerate: string
updated_at: timestamp[s]
completed: int64
total: int64
status: string
job_id: string
current_case: null
model: string
failed: list<item: null>
child 0, item: null
to
{'status': Value('string'), 'model': Value('string'), 'total': Value('int64'), 'completed': Value('int64'), 'failed': List(Value('null')), 'job_id': Value('string'), 'updated_at': Value('timestamp[s]'), 'current_case': Value('null')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
identity: struct<settings: struct<model: string, repo_id: string, pipeline: string, num_inference_steps: int64 (... 281 chars omitted)
child 0, settings: struct<model: string, repo_id: string, pipeline: string, num_inference_steps: int64, guidance_scale: (... 209 chars omitted)
child 0, model: string
child 1, repo_id: string
child 2, pipeline: string
child 3, num_inference_steps: int64
child 4, guidance_scale: double
child 5, max_sequence_length: int64
child 6, seed: int64
child 7, generator_device: string
child 8, width: int64
child 9, height: int64
child 10, dtype: string
child 11, quantization: null
child 12, attention: string
child 13, model_revision: string
child 14, resolution_source: string
child 1, prompts_sha256: string
child 2, case_ids: list<item: string>
child 0, item: string
python: string
slurm_job_id: string
cuda_visible_devices: string
versions: struct<torch: string, diffusers: string, transformers: string, accelerate: string>
child 0, torch: string
child 1, diffusers: string
child 2, transformers: string
child 3, accelerate: string
updated_at: timestamp[s]
completed: int64
total: int64
status: string
job_id: string
current_case: null
model: string
failed: list<item: null>
child 0, item: null
to
{'status': Value('string'), 'model': Value('string'), 'total': Value('int64'), 'completed': Value('int64'), 'failed': List(Value('null')), 'job_id': Value('string'), 'updated_at': Value('timestamp[s]'), 'current_case': Value('null')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
IGenBench_result
Z-Image-Turbo、FLUX.1-dev 和 HiDream-I1-Full 在 IGenBench 指定 100 个 case 上的图片生成结果。每个模型 100 张，共 300 张，分辨率均为 1024 × 1024。
| 模型 | 下载文件 | 图片数量 |
|---|---|---|
| Z-Image-Turbo | Z-Image-Turbo_IGenBench.zip | 100 |
| FLUX.1-dev | FLUX.1-dev_IGenBench.zip | 100 |
| HiDream-I1-Full | HiDream-I1-Full_IGenBench.zip | 100 |
每个 ZIP 保留对应模型文件夹，包含 PNG 图片、case JSON、每张图片的 generation_parameters.json 和整组结果的校验记录。所有图片已经通过文件完整性和 100 个 case 的覆盖检查。
生成使用 BF16、无量化，采用各模型官方示例参数。Z-Image-Turbo 为 9 步、guidance 0.0、seed 42；FLUX.1-dev 为 50 步、guidance 3.5、seed 0；HiDream-I1-Full 为 50 步、guidance 5.0、seed 0。原始 prompt 保留；超过模型编码长度的内容由模型原生截断，相关 token 数和截断情况记录在每张图片的生成参数文件中。
本数据集提供图片生成结果，尚未计算 Q-ACC 或 I-ACC 分数。文件大小、SHA-256 和完整参数见 manifest.json。
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