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
environment: struct<config: struct<checkpoint: string, t5_checkpoint: string, tokenizer: string, style_token: str (... 3065 chars omitted)
child 0, config: struct<checkpoint: string, t5_checkpoint: string, tokenizer: string, style_token: string, initialize (... 393 chars omitted)
child 0, checkpoint: string
child 1, t5_checkpoint: string
child 2, tokenizer: string
child 3, style_token: string
child 4, initializer: string
child 5, vectors: int64
child 6, lr: double
child 7, noise_shift: double
child 8, max_grad_norm: double
child 9, cpu_threads: int64
child 10, seed: int64
child 11, prompt: string
child 12, save_every: int64
child 13, checkpoint_policy: string
child 14, uncheckpointed_blocks: int64
child 15, fp32_text_context: bool
child 16, fused_rmsnorm: bool
child 17, fused_rope: bool
child 18, experiment: string
child 19, appearance_enabled: bool
child 20, lora_rank: int64
child 21, lora_alpha: int64
child 22, lora_lr: double
child 23, lora_dropout: double
child 1, checkpoint: struct<step: int64, conditioning: struct<cond_dim: null, adapter_type: string, adapter_hidden_dim: n (... 1948 chars omitted)
child 0, step: int64
child 1, conditioning: struct<cond_dim: null, adapter_type: string, adapter_hidden_dim: null, interaction_init: string, ful (... 1750 chars omitted)
child 0, cond_dim: null
child
...
_no_grad: bool
ddp_replicas_identical: bool
mean_step_seconds: double
mean_wall_seconds: double
mean_forward_seconds: double
mean_backward_seconds: double
mean_clips_per_second: double
warmup: bool
gradient_rank_max_abs_difference: double
global_batch_size: int64
backward_seconds: double
peak_gpu_gib: double
clips_per_second: double
ranks: list<item: struct<loss: double, grad_norm: double, vector_grad_norms: list<item: double>, embedding_ (... 343 chars omitted)
child 0, item: struct<loss: double, grad_norm: double, vector_grad_norms: list<item: double>, embedding_update_norm (... 331 chars omitted)
child 0, loss: double
child 1, grad_norm: double
child 2, vector_grad_norms: list<item: double>
child 0, item: double
child 3, embedding_update_norm: double
child 4, lora_b_grad_norm: double
child 5, forward_seconds: double
child 6, backward_seconds: double
child 7, optimizer_and_checks_seconds: double
child 8, step_seconds: double
child 9, peak_gpu_gib: double
child 10, output_shape: list<item: int64>
child 0, item: int64
child 11, frozen_parameters_have_no_grad: bool
child 12, rank: int64
child 13, sample_indices: list<item: int64>
child 0, item: int64
child 14, data_seconds: double
child 15, wall_seconds: double
optimizer_step: int64
step_seconds: double
parameter_rank_max_abs_difference: double
forward_seconds: double
wall_seconds: double
loss: double
to
{'optimizer_step': Value('int64'), 'warmup': Value('bool'), 'loss': Value('float64'), 'global_batch_size': Value('int64'), 'ranks': List({'loss': Value('float64'), 'grad_norm': Value('float64'), 'vector_grad_norms': List(Value('float64')), 'embedding_update_norm': Value('float64'), 'lora_b_grad_norm': Value('float64'), 'forward_seconds': Value('float64'), 'backward_seconds': Value('float64'), 'optimizer_and_checks_seconds': Value('float64'), 'step_seconds': Value('float64'), 'peak_gpu_gib': Value('float64'), 'output_shape': List(Value('int64')), 'frozen_parameters_have_no_grad': Value('bool'), 'rank': Value('int64'), 'sample_indices': List(Value('int64')), 'data_seconds': Value('float64'), 'wall_seconds': Value('float64')}), 'forward_seconds': Value('float64'), 'backward_seconds': Value('float64'), 'step_seconds': Value('float64'), 'wall_seconds': Value('float64'), 'peak_gpu_gib': Value('float64'), 'parameter_rank_max_abs_difference': Value('float64'), 'gradient_rank_max_abs_difference': Value('float64'), 'clips_per_second': Value('float64')}
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
environment: struct<config: struct<checkpoint: string, t5_checkpoint: string, tokenizer: string, style_token: str (... 3065 chars omitted)
child 0, config: struct<checkpoint: string, t5_checkpoint: string, tokenizer: string, style_token: string, initialize (... 393 chars omitted)
child 0, checkpoint: string
child 1, t5_checkpoint: string
child 2, tokenizer: string
child 3, style_token: string
child 4, initializer: string
child 5, vectors: int64
child 6, lr: double
child 7, noise_shift: double
child 8, max_grad_norm: double
child 9, cpu_threads: int64
child 10, seed: int64
child 11, prompt: string
child 12, save_every: int64
child 13, checkpoint_policy: string
child 14, uncheckpointed_blocks: int64
child 15, fp32_text_context: bool
child 16, fused_rmsnorm: bool
child 17, fused_rope: bool
child 18, experiment: string
child 19, appearance_enabled: bool
child 20, lora_rank: int64
child 21, lora_alpha: int64
child 22, lora_lr: double
child 23, lora_dropout: double
child 1, checkpoint: struct<step: int64, conditioning: struct<cond_dim: null, adapter_type: string, adapter_hidden_dim: n (... 1948 chars omitted)
child 0, step: int64
child 1, conditioning: struct<cond_dim: null, adapter_type: string, adapter_hidden_dim: null, interaction_init: string, ful (... 1750 chars omitted)
child 0, cond_dim: null
child
...
_no_grad: bool
ddp_replicas_identical: bool
mean_step_seconds: double
mean_wall_seconds: double
mean_forward_seconds: double
mean_backward_seconds: double
mean_clips_per_second: double
warmup: bool
gradient_rank_max_abs_difference: double
global_batch_size: int64
backward_seconds: double
peak_gpu_gib: double
clips_per_second: double
ranks: list<item: struct<loss: double, grad_norm: double, vector_grad_norms: list<item: double>, embedding_ (... 343 chars omitted)
child 0, item: struct<loss: double, grad_norm: double, vector_grad_norms: list<item: double>, embedding_update_norm (... 331 chars omitted)
child 0, loss: double
child 1, grad_norm: double
child 2, vector_grad_norms: list<item: double>
child 0, item: double
child 3, embedding_update_norm: double
child 4, lora_b_grad_norm: double
child 5, forward_seconds: double
child 6, backward_seconds: double
child 7, optimizer_and_checks_seconds: double
child 8, step_seconds: double
child 9, peak_gpu_gib: double
child 10, output_shape: list<item: int64>
child 0, item: int64
child 11, frozen_parameters_have_no_grad: bool
child 12, rank: int64
child 13, sample_indices: list<item: int64>
child 0, item: int64
child 14, data_seconds: double
child 15, wall_seconds: double
optimizer_step: int64
step_seconds: double
parameter_rank_max_abs_difference: double
forward_seconds: double
wall_seconds: double
loss: double
to
{'optimizer_step': Value('int64'), 'warmup': Value('bool'), 'loss': Value('float64'), 'global_batch_size': Value('int64'), 'ranks': List({'loss': Value('float64'), 'grad_norm': Value('float64'), 'vector_grad_norms': List(Value('float64')), 'embedding_update_norm': Value('float64'), 'lora_b_grad_norm': Value('float64'), 'forward_seconds': Value('float64'), 'backward_seconds': Value('float64'), 'optimizer_and_checks_seconds': Value('float64'), 'step_seconds': Value('float64'), 'peak_gpu_gib': Value('float64'), 'output_shape': List(Value('int64')), 'frozen_parameters_have_no_grad': Value('bool'), 'rank': Value('int64'), 'sample_indices': List(Value('int64')), 'data_seconds': Value('float64'), 'wall_seconds': Value('float64')}), 'forward_seconds': Value('float64'), 'backward_seconds': Value('float64'), 'step_seconds': Value('float64'), 'wall_seconds': Value('float64'), 'peak_gpu_gib': Value('float64'), 'parameter_rank_max_abs_difference': Value('float64'), 'gradient_rank_max_abs_difference': Value('float64'), 'clips_per_second': Value('float64')}
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.
CS:GO 四风格配对数据
本仓库以原始CSGO、日漫、向日葵、万圣节为主要风格，收录图片、视频及其可追溯来源。原始CSGO是真实引擎RGB；三种艺术风格是派生目标。白模属于条件输入，人物与武器设计图属于参考素材，不能作为额外训练样本计数。
| 风格 | 数据入口 | 最终演示与来源映射 |
|---|---|---|
| 原始CSGO | original/batches | 1min-demo |
| 日漫（糸守/你的名字） | anime | anime/demo-used-cases |
| 向日葵（金黄/钴蓝厚涂） | sunflowers | sunflowers/demo-used-cases |
| 万圣节（骷髅女巫） | halloween | halloween/demo-used-cases |
最新成对图片
2026-10-11：42个完整组，126张风格化目标，42张原始CSGO、42张白模输入，共210张PNG。
每组包含相同真实源帧对应的白模、原画、日漫、向日葵、万圣节图片；实际提示词、输出版本、源revision/tar/frame/tick和条件行绑定均保留。1组用户批准、41组父级视觉审核通过。本次只上传完成交付快照；生成中、仅自检、判退与隔离候选不计入完成量。正在推进的200组/600张总目标尚未完成。
视频与完整条件
视频按各风格目录的不可变 batches/<version>/ 管理；最终演示在 demo-used-cases/，原始视频和完整条件在 original/batches/ 与 1min-demo/。时长按配对时间线计一次，白模、RGB、多风格目标不重复相加。每个批次的manifest与verification是实际数量和审核状态的依据。
完整原生条件至少遵循external_v2（第二版外部训练协议）：RGB、Dense（逐时刻空间条件）、State（人物/武器状态）、Interaction（交互）、Pose（姿态）、相机内外参及时间身份绑定。派生图片/视频通过固定源版本映射复用完整条件，不能把RGB-only缓存当完整训练数据。视频生成目标与源FPS可能不同，必须按时间重采样，不能直接按帧号对应。
视觉审核通过不代表生成像素、三维几何或碰撞严格对齐。本次图片保留 training_ready=false（严格训练资格未认证）；各视频的审核/来源认证以各批清单为准。episode-09及同回放全视角维持验证隔离，不能混入此图片训练候选；不要按相邻帧随机切分。
参考、索引与历史
- 四风格机器可读索引：图片、视频和设计参考入口。
- 人物与武器设计图：批准外观参考，非训练配对图。
- 既有原始采集目录：保留原索引和不可变路径。
- 原数据卡与详细协议/历史审计：完整保留此前说明；其中日期较早的数量和“有效数据为0”属于当时审计，不代表当前仓库为空。
- 历史星月夜实验与历史毕加索实验：保留既有内容，未并入四种主要风格完成统计。
- 审计、模型、处理缓存及历史demo保持原路径。
原始游戏及相关角色/美术权利归相应权利人。本仓库不新增许可声明，也不保证第三方资产可用于商业用途。
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