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faraway00
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stylization-csgo-data

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Tags:
image-to-image
video-to-video
csgo
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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:    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
peak_gpu_gib: double
clips_per_second: double
parameter_rank_max_abs_difference: double
wall_seconds: 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
warmup: bool
gradient_rank_max_abs_difference: double
backward_seconds: double
step_seconds: double
global_batch_size: int64
loss: double
optimizer_step: int64
forward_seconds: 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
              peak_gpu_gib: double
              clips_per_second: double
              parameter_rank_max_abs_difference: double
              wall_seconds: 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
              warmup: bool
              gradient_rank_max_abs_difference: double
              backward_seconds: double
              step_seconds: double
              global_batch_size: int64
              loss: double
              optimizer_step: int64
              forward_seconds: 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

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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。

批次文件与说明 · 逐组配对索引 · 内容校验值。

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。

最新原生 B 区 2v2 连续采集

截至 2026-10-12 10:22（北京时间），上传队列远端校验回执确认已交付 64 条完整玩家视角序列，其中 16 组对局的四个视角均已交付。 每条序列为 120 秒；本轮已交付文件总量约 650.5 GB（十进制，包含条件数据、归档及处理代码）。这是时间点快照，后续采集仍在继续。

本轮正式数据目录

  • 对局时间：32 分钟，同一对局的四个同步视角计一次。
  • 累计玩家视角视频时长：128 分钟，按每条已交付序列计 2 分钟；多视角不能作为独立对局计数。
  • 目录按 native_b_<编号>-p0 至 -p3 分别存放四个玩家视角；前两组修复交付使用 native_b_000001r5、native_b_000002r7 标识。
  • 每条序列包含 24 个 chunk-00.tar 至 chunk-23.tar 分块归档，以及 delivery.json、progress.json、动作、事件、状态导出和处理代码等文件。内容格式和完整性以该序列的清单及归档内文件为准。
  • 正式上传仅接收 delivery.json 标记 PASS_ALL_REQUIRED_QA 的完整 120 秒序列；交付后核对远端文件大小及 SHA256/LFS 或 Git blob 校验值。该交付检查不替代各训练任务自己的资格验证。
  • 上述完成量不计未完成序列、失败原始尝试和历史批次，不重复相加 RGB、白模、深度、法线或其他条件模态。

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

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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