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luuuulinnnn
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3D_Brain_partsLDM-controlnet-inference

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The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
                                                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
                  cls = get_filesystem_class(protocol)
                File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
                  raise ValueError(f"Protocol not known: {protocol}")
              ValueError: Protocol not known: memory
              
              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/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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3D_Brain_partsLDM — ControlNet inference samples

Generated (synthetic) whole-brain MRI samples from the spacing-1.5 ControlNet comparison suite built on 3D_Brain_partsLDM. Each variant produced 291 samples × 2 control scales, 50 DDIM steps, target-free reverse sampling (scaffold_mode = sample).

file	variant	description
`b1_final_291.tar.gz`	B1	frozen-backbone ControlNet baseline (whole UNet frozen)
`b2_final_291.tar.gz`	B2	jointly-trained ControlNet baseline
`o1_final_291.tar.gz`	O1	ControlNet + compositional aux-t_aux (the method; part experts frozen)
`o2_final_291.tar.gz`	O2	O1 + maskLDM 16-channel intensity context (proposed improvement)

Each archive expands to:

<variant>_final_291/
  inference_manifest.json
  control_scale_1/   images/*.nii.gz (291)  +  manifest.jsonl    # ControlNet ON
  control_scale_0/   images/*.nii.gz (291)  +  manifest.jsonl    # ControlNet OFF (same seeds)

control_scale_1 vs control_scale_0 form the within-checkpoint causal ablation: identical seeds/backbone, with scale 0 suppressing the injected ControlNet residuals.

Provenance

Grid 1×128×128×128 @ 1.5mm isotropic; whole latent 8×32×32×32.
Fixed template-mask conditioning: lhemi, rhemi, sub (dilated_r2).
Training: 8×H200 DDP, 2500 optimizer updates (data-parallel equivalent of the 20k single-GPU budget).
These are model generations, not real ADNI scans. Set the repository license per your data-use agreement.

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Total file size:

15.6 GB

3D_Brain_partsLDM — ControlNet inference samples

Contents

Provenance