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Datasets:
World-Snapshot
/
MAGICK_Transparency

Tasks:
Image Segmentation
Image-to-Image
Tags:
transparency
alpha-matting
layer-diffuse
magick
wsm
License:
Dataset card Data Studio Files
xet
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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
dataset_name: string
source_path: string
repo_id: string
created_at: timestamp[s]
file_count: int64
total_size: int64
parts: list<item: struct<part: string, file_count: int64, total_size: int64>>
  child 0, item: struct<part: string, file_count: int64, total_size: int64>
      child 0, part: string
      child 1, file_count: int64
      child 2, total_size: int64
path: string
part: string
size: int64
to
{'part': Value('string'), 'path': Value('string'), 'size': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                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
              dataset_name: string
              source_path: string
              repo_id: string
              created_at: timestamp[s]
              file_count: int64
              total_size: int64
              parts: list<item: struct<part: string, file_count: int64, total_size: int64>>
                child 0, item: struct<part: string, file_count: int64, total_size: int64>
                    child 0, part: string
                    child 1, file_count: int64
                    child 2, total_size: int64
              path: string
              part: string
              size: int64
              to
              {'part': Value('string'), 'path': Value('string'), 'size': Value('int64')}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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part
string
path
string
size
int64
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/035XJHeSyQ.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/082nlZHaW7.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/0FsmVdrAPZ.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/0HkzD3vKgv.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/0QC2KsZTlH.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/0X4TmKfWlr.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/0X5lZNcT9T.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/0YUjfygKlf.wsg.npz
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/3OcSnrm9AI.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/3UgpD7UlFN.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/3aIP41Arii.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/4V2An8lSlp.wsg.npz
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/4yrZuMhNU3.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5B9GhsRAZe.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5C8sbEHTHV.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5CNUel71Yc.wsg.npz
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5KoxaEWJLQ.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5bhOtn0hqd.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5c21WFlD2m.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5c6RuqelRN.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5enPE5WIrf.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5goonTNsTb.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/5m23F4odEf.wsg.npz
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MAGICK_Transparency_part001.tar.zst
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/6BOt9IiEKd.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/6TSgNuG2vj.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/6haWmjBI95.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/6zZyMCBYIH.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/79gIEa2F0V.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7G1LSUKCGK.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7QBK4iRJkm.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7aJ0q7fe06.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7bTgHS5T1z.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7cOtjsPsCr.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7cmfwb4cSk.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7fDmQOFbWq.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7vF4lUVeWP.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/7zJhtL5HL0.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/82Sa1iICix.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/84DWrK38dP.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8BrCseCvRE.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8CtcGOX5SG.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8GIsvKraal.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8H61bwcKMZ.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8JljEPGcri.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8WmaJaXSnc.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8ghKs9e0P4.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8rb16J7rZR.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/8zwCjB66EO.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/9062hAyScs.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/95VpZ4qU42.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/9I5I45DLQL.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/9MNJEOXjui.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/9hAVzZ4Gi0.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/9i6VAOEr2d.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/9rWdYuHGUq.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/9zSbHhcvGG.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/AAzn6Pr0h1.wsg.npz
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MAGICK_Transparency_part001.tar.zst
wsg_named/animals/ABmNE1FTpz.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/AK1uzI27qV.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/AMnDFtkXhA.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/ANPaYdMy8f.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/ATtIQINNZH.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/Ae4jip6ov3.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/Af1rRIBN3g.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/AhGjFZDj6F.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/AkiIlvMEsf.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/ApGmxnliZT.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/B5TLfuGXo1.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/BRJrWDgoDh.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/BZXkCUgHbF.wsg.npz
3,686,942
MAGICK_Transparency_part001.tar.zst
wsg_named/animals/BcUZWcRJaQ.wsg.npz
3,686,942
End of preview.

MAGICK Transparency — raw WSG samples

Raw (pre-VAE) samples for the WSM v1 transparency-estimation pilot (magick_transparency_v1): predict a foreground alpha / transparency layer from an RGB image. The matching FLUX.2 latents live in the companion latent repo World-Snapshot/WSM_v1_Latent_PT_WSG_v1.8_for_Flux2 (custom_pilots/magick_transparency_v1/cache_named/).

Source

Built from the MAGICK RGBA asset set (transparent foreground objects). Each foreground is composited over random backgrounds so the model must recover the soft alpha of the object from a realistic RGB image.

How each sample is produced

Each self-describing named-key .npz holds two modalities (uint8, H×W×3; the trainer maps pixels with x/127.5 − 1):

key meaning
0-2 RGB — the foreground composited over a random background.
LayerDiffuse_Transparency target — the RGB-replicated soft alpha / transparency of the foreground (1-channel alpha stored replicated to 3 channels).

This is an estimation task: RGB → transparency. No caption, no reference image.

Layout 

wsg_named/     the named-key .npz samples (0-2 + LayerDiffuse_Transparency)

split_manifest.jsonl records the deterministic train/val/test assignment used by WSM v1 training (see split_summary.json); the split is per-sample (npz path), reproducing the trainer's hash split at val_split_ratio=0.05.

Packaging

Streamed as multi-part MAGICK_Transparency_partNNN.tar.zst (each < 15 GB) with ..._parts_manifest.jsonl (path → part index) and ..._parts_summary.json.

Extract 

mkdir -p data/custom_pilots/magick_transparency_v1
for f in MAGICK_Transparency_part*.tar.zst; do
  tar -xf "$f" -C data/custom_pilots/magick_transparency_v1/
done
# -> data/custom_pilots/magick_transparency_v1/wsg_named/

License

MIT for the packaging/processing; underlying MAGICK assets follow their original terms.
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