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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:    ValueError
Message:      Invalid string class label my_train_data@d5d0662840daeb6569918f60e025fc3f3e7c7edd
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label my_train_data@d5d0662840daeb6569918f60e025fc3f3e7c7edd

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Anime LoKR Training Dataset

这是一个用于 Anime LoKR(Low-Rank Adaptation of Kronecker Product)微调的动漫风格图像训练集。

数据集简介

  • 主要用途: 用于训练/微调 Stable Diffusion (如 SDXL 或高分辨率 SD 1.5) 的动漫风格 LoKR 或 LyCORIS 模型。
  • 图像分辨率: 1024x1024 像素。
  • 数据格式: 每张图像(.png.jpg)均配有同名的文本标签文件(.txt),包含训练所需的 tag/caption。

目录结构参考

你可以参考以下结构组织本仓库的文件(如果使用的是 Kohya-ss 等主流训练工具):

my_train_data/
├── 10_concept_name/       # 训练文件夹(数字代表重复次数,concept_name为触发词或概念)
│   ├── 0001.png           # 1024x1024 训练图像
│   ├── 0001.txt           # 对应的 tag 描述文件
│   ├── 0002.png
│   └── 0002.txt
└── README.md

训练建议

  • 推荐基础模型: 鉴于 1024 的高分辨率,本数据集非常适合用于 SDXL 的微调。如果应用于 SD 1.5,建议在训练脚本中开启宽高比分桶(Aspect Ratio Bucketing)。
  • 推荐工具: Kohya-ss GUILyCORIS 训练框架。
  • LoKR 常用参数参考:
    • network_module: lycoris.kohya
    • algo: lokr
    • 根据显存和拟合需要调节 network_dim (rank) 和 network_alpha

许可与免责声明

(请根据你数据集的图片来源,在此处补充相关的版权说明或使用限制)

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