Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'validation' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 342, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 838, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4645, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2866, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 345, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

BizGenEval_result

10 个模型在 BizGenEval 完整 400 个 case 上的图片生成结果,每个模型 400 张,共 4,000 张。这里只提供生成图片,尚未计算 benchmark 分数。

模型 下载文件 图片数量 压缩包大小
Z-Image-Turbo Z-Image-Turbo_BizGenEval.zip 400 1075.5 MB
FLUX.1-dev FLUX.1-dev_BizGenEval.zip 400 1180.4 MB
HiDream-I1-Full HiDream-I1-Full_BizGenEval.zip 400 278.4 MB

2026-11-10 新增结果

新增七个模型,各 400 张,共 2,800 张。

模型 下载文件 原始图片数 大小
Qwen-Image-2.1 Qwen-Image-2.1_BizGenEval.zip 400 3.22 GB
Qwen-Image-2.1-Turbo Qwen-Image-2.1-Turbo_BizGenEval.zip 400 2.92 GB
FLUX-3-Image-Klein FLUX-3-Image-Klein_BizGenEval.zip 400 0.47 GB
SenseNova-U1.5-8B-MoT SenseNova-U1.5-8B-MoT_BizGenEval.zip 400 1.71 GB
Fibo-2.0-dev Fibo-2.0-dev_BizGenEval.zip 400 0.37 GB
Fibo-2.0-edit-turbo Fibo-2.0-edit-turbo_BizGenEval.zip 400 0.30 GB
JoyAI-Image-Edit-Plus JoyAI-Image-Edit-Plus_BizGenEval.zip 400 0.44 GB

每个 ZIP 解压得到一个模型文件夹,根目录下是 400 张原始 PNG,metadata/ 保存逐图 prompt、实际尺寸和生成参数。Qwen 两个模型保留原始 RGBA,同时在 rgb_exports/ 提供直接取 RGB 通道的导出,没有叠加白底。其他模型原图已是 RGB。RGB 导出不是额外 case。

下载并解压某个模型,例如:

pip install -U huggingface_hub
hf download yomi-chen/BizGenEval_result Qwen-Image-2.1_BizGenEval.zip --repo-type dataset --local-dir results
unzip results/Qwen-Image-2.1_BizGenEval.zip -d results/generated_images

将文件名换成表中的其他 ZIP 即可。后续用 BizGenEval 评分时,普通 RGB 模型使用 results/generated_images/<模型名>;Qwen 若评分器要求 RGB,使用对应模型的 rgb_exports/。

模型 步数 分辨率设置
Qwen-Image-2.1 40 约 4MP,按 case 长宽比
Qwen-Image-2.1-Turbo 8 约 4MP,按 case 长宽比
FLUX-3-Image-Klein 16 约 1MP,按 case 长宽比
SenseNova-U1.5-8B-MoT 50 最接近 case 长宽比的官方 2048 桶
Fibo-2.0-dev 50 约 1MP,按 case 长宽比
Fibo-2.0-edit-turbo 4 约 1MP,按 case 长宽比
JoyAI-Image-Edit-Plus 30 约 1MP,按 case 长宽比

七组均使用 seed 42、BF16,保留生成尺寸,不做生成后的缩放。FLUX 使用一次官方本地 prompt upsampler,原始及扩写 prompt 均保存在元数据中。Fibo Dev 直接使用 benchmark 原始自然语言 prompt,没有额外 VGL/API 改写。

Fibo Edit Turbo 和 JoyAI 是编辑模型的纯文本输入尝试:只输入 prompt,不输入原图。这两组应与原生文生图模型分开解释。

早期三个模型:文件内容

每个 ZIP 保留对应模型文件夹,包含 400 张 PNG、metadata/ 中每个 case 的原始数据与生成参数,以及 run_manifest.json、live_progress.json、verification.json。PNG 文件名沿用 benchmark 的格式,便于后续评测。

早期三个模型:生成配置

使用本地权重、BF16、无量化,环境为 PyTorch 2.8.0+cu128、Diffusers 0.41.0.dev0、Transformers 5.19.0。

模型 推理步数 Guidance Seed 最大文本编码长度
Z-Image-Turbo 9 0.0 42 512
FLUX.1-dev 50 3.5 0 512
HiDream-I1-Full 50 5.0 0 128

原始 prompt 保留,模型各文本编码器按其原生规则截断;token 数及截断情况保存在每张图的元数据中。

Z-Image-Turbo 和 FLUX.1-dev 按 BizGenEval 的 dynamic_original 策略解析尺寸:优先使用参考图尺寸,缺失时使用指定长宽比,按 32 像素对齐,最多 4,194,304 像素。HiDream-I1-Full 的官方 Pipeline 会进一步按其原生 1,048,576 像素预算和 16 像素步长归一化尺寸。三组结果因此没有统一固定为 1024×1024;每张图的实际尺寸见元数据中的 dimensions,请求尺寸见 requested_dimensions(提供时)。

这里提供图片生成结果,尚未计算 Gemini checklist 评测分数。完整生成参数见 manifest.json。

Downloads last month
74