diff --git a/biahub/flat_field_correction.py b/biahub/flat_field_correction.py
index 0695e3dc..996aa44f 100644
--- a/biahub/flat_field_correction.py
+++ b/biahub/flat_field_correction.py
@@ -247,7 +247,7 @@ def flat_field_correction_cli(
     monitor: bool = False,
     init_only: bool = False,
 ):
-    r"""Apply flat field correction across T and selected C axes.
+    """Apply flat field correction across T and selected C axes.
 
     \b
     SLURM fan-out of positions across a whole plate:
@@ -260,7 +260,7 @@ def flat_field_correction_cli(
     \b
     In-process run of a single position (e.g. from a Nextflow worker):
     >>> biahub flat-field --cluster debug -i ./input.zarr/A/1/0 -c ./flat_field_params.yml -o ./output.zarr
-    """
+    """  # noqa: D301
     flat_field(
         input_position_dirpaths=input_position_dirpaths,
         config_filepath=config_filepath,
diff --git a/biahub/virtual_stain.py b/biahub/virtual_stain.py
index bba2fd4a..8210805e 100644
--- a/biahub/virtual_stain.py
+++ b/biahub/virtual_stain.py
@@ -1,441 +1,449 @@
-import os
-import shutil
-import subprocess
+import time
 
 from pathlib import Path
+from typing import Literal
 
 import click
 import numpy as np
 import submitit
+import torch
 
 from iohub.ngff import open_ome_zarr
 from iohub.ngff.utils import create_empty_plate
 
+from biahub.cli import utils
 from biahub.cli.monitor import monitor_jobs
 from biahub.cli.parsing import (
+    cluster,
+    config_filepath,
+    init_only,
     input_position_dirpaths,
-    local,
     monitor,
-    num_processes,
     output_dirpath,
-    sbatch_filepath_predict,
-    sbatch_filepath_preprocess,
+    sbatch_filepath,
     sbatch_to_submitit,
 )
-from biahub.cli.utils import get_submitit_cluster
+from biahub.cli.utils import (
+    get_submitit_cluster,
+    resolve_ome_zarr_version,
+)
 
 
-def run_viscy_preprocess(
-    data_path: str,
-    num_workers: int = 32,
-    config_file: str = None,
-    path_viscy_env: Path = None,
-    verbose: bool = False,
-):
-    """
-    Run VisCy preprocess on a single FOV.
+def build_predict_parser():
+    """Build a jsonargparse parser that mirrors the ``viscy predict`` config.
 
-    Parameters
-    ----------
-    data_path : str
-        Path to the FOV data.
-    num_workers : int
-        Number of workers to use.
-    config_file : str
-        Path to the VisCy config file.
-    path_viscy_env : Path
-        Path to the VisCy environment.
-    verbose : bool
-        Whether to print verbose output.
+    The model and data blocks are registered as subclass arguments of VisCy's
+    own ``VSUNet`` and ``HCSDataModule`` classes, so the configuration is
+    validated against VisCy's actual class signatures -- the same machinery the
+    ``viscy predict`` CLI uses. biahub therefore keeps no copy of the model
+    schema that could fall out of date when VisCy changes upstream.
+
+    The biahub-specific orchestration knobs (``sliding_window_step``,
+    ``device``, ``output_ome_zarr_version``) and the top-level ``ckpt_path``
+    are added as plain arguments. ``data.init_args.data_path`` is intentionally
+    left out of the config file -- biahub injects the position path per job.
     """
-    cmd = (
-        "module load anaconda && "
-        f"conda activate {path_viscy_env} && "
-        f"viscy preprocess "
-        f"--data_path {data_path} "
-        f"--num_workers {num_workers} "
+    import jsonargparse
+
+    from cytoland.engine import VSUNet
+    from viscy_data.hcs import HCSDataModule
+
+    parser = jsonargparse.ArgumentParser()
+    parser.add_argument("--config", action="config")
+    parser.add_subclass_arguments(VSUNet, "model")
+    parser.add_subclass_arguments(HCSDataModule, "data")
+    parser.add_argument("--ckpt_path", type=str, required=True)
+    parser.add_argument("--sliding_window_step", type=int, default=1)
+    parser.add_argument("--device", type=str, default="cuda")
+    # Output OME-Zarr version: None preserves the input store's version.
+    parser.add_argument(
+        "--output_ome_zarr_version",
+        type=Literal["0.4", "0.5"] | None,
+        default=None,
     )
-    if config_file:
-        cmd += f" -c {config_file}"
-    else:
-        cmd += "--channel_names -1  --block_size 32"
-    if verbose:
-        click.echo(f"Preprocess FOV: {'/'.join(Path(data_path).parts[-3:])}")
-        click.echo(f"Command: {cmd}")
-    subprocess.run(cmd, shell=True, check=True, executable="/bin/bash")
-
-
-def run_viscy_predict(
-    data_path: str,
-    config_file: str,
-    output_store: str,
-    log_dir: str,
-    path_viscy_env: Path = None,
-    verbose: bool = False,
-):
-    """
-    Run VisCy predict on a single FOV.
+    return parser
+
+
+def load_predict_config(config_filepath: Path, data_path: Path):
+    """Validate a VisCy predict config and inject the per-position ``data_path``.
 
     Parameters
     ----------
-    data_path : str
-        Path to the FOV data.
-    config_file : str
-        Path to the VisCy config file.
-    output_store : str
-        Path to the output store.
-    log_dir : str
-        Path to the log directory.
-    path_viscy_env : Path
-        Path to the VisCy environment.
-    verbose : bool
-        Whether to print verbose output.
-
+    config_filepath : Path
+        Path to the VisCy-style predict YAML config.
+    data_path : Path
+        Position path injected as ``data.init_args.data_path`` (biahub supplies
+        this per job; the first position is used for submit-time validation).
+
+    Returns
+    -------
+    tuple
+        ``(parser, cfg)`` where ``cfg`` is the validated config namespace.
     """
-    os.chdir(log_dir)
-    # Compose the shell command
-    cmd = (
-        "module load anaconda && "
-        f"conda activate {path_viscy_env} && "
-        "viscy predict "
-        f'-c "{config_file}" '
-        f'--data.init_args.data_path "{data_path}" '
-        f"--trainer.callbacks+=viscy.translation.predict_writer.HCSPredictionWriter "
-        f'--trainer.callbacks.output_store "{output_store}" '
-        f'--trainer.default_root_dir "{log_dir}"'
+    parser = build_predict_parser()
+    cfg = parser.parse_args(
+        [
+            "--config",
+            str(config_filepath),
+            "--data.init_args.data_path",
+            str(data_path),
+        ]
     )
-    if verbose:
-        click.echo(f"Predict FOV: {'/'.join(Path(data_path).parts[-3:])}")
-        click.echo(f"Command: {cmd}")
-    subprocess.run(cmd, shell=True, check=True, executable="/bin/bash")
+    return parser, cfg
 
 
-def combine_fov_zarrs_to_plate(
-    fovs: list[Path],
-    temp_dir: Path,
-    output_dirpath: Path,
-    cleanup: bool = True,
+def virtual_stain_position(
+    config_filepath: Path,
+    input_position_path: Path,
+    output_position_path: Path,
 ):
-    """
-    Combine VisCy-predicted FOV Zarrs (in temp) into a single HCS plate Zarr by moving.
+    """Run cytoland virtual staining on a single position, looping over time.
+
+    Inference is GPU-bound, so timepoints are processed serially in a ``for``
+    loop rather than in parallel. Each timepoint volume is normalized with the
+    config's ``NormalizeSampled`` transforms (using the precomputed statistics
+    in the store) and passed through
+    ``AugmentedPredictionVSUNet.predict_sliding_windows`` -- the same linear
+    feathering blend used by the ``viscy predict`` ``HCSPredictionWriter``.
+
+    Normalization and test-time augmentation are delegated to VisCy
+    (``NormalizeSampled`` / ``read_norm_meta`` / ``with_rotation_tta``) so the
+    behavior stays in sync with ``viscy predict``.
 
     Parameters
     ----------
-    fovs : list of Path
-        Original FOV paths (used to extract B/1/000000).
-    temp_dir : Path
-        Directory containing the individual .zarr folders (each named like B_1_000000.zarr).
-    output_dirpath : Path
-        The plate-level HCS Zarr to merge into.
-    cleanup : bool
-        Whether to delete the moved files afterwards. Default True.
+    config_filepath : Path
+        Path to the VisCy-style predict config.
+    input_position_path : Path
+        Input position (label-free source) to virtually stain.
+    output_position_path : Path
+        Output position to write predictions into.
     """
-    for fov in fovs:
-        row, col, pos = fov.parts[-3:]
-        nested_fov_path = temp_dir / f"{row}_{col}_{pos}.zarr" / row / col / pos
+    from cytoland.engine import AugmentedPredictionVSUNet
+    from monai.transforms import Compose
+    from viscy_data import read_norm_meta
+
+    position_name = "/".join(Path(input_position_path).parts[-3:])
+    position_start = time.perf_counter()
+    parser, cfg = load_predict_config(config_filepath, input_position_path)
+
+    device = torch.device(
+        cfg.device if (cfg.device == "cpu" or torch.cuda.is_available()) else "cpu"
+    )
+    click.echo(f"[{position_name}] Starting virtual staining on device '{device}'")
+
+    # Instantiate the VSUNet and the data module from VisCy's own classes.
+    # Route the top-level ckpt_path into the model's init args so VSUNet loads
+    # the weights itself: VSUNet.__init__ extracts the Lightning checkpoint's
+    # "state_dict" exactly as `viscy predict` does. Delegating keeps biahub free
+    # of both the model schema and any assumption about the checkpoint layout.
+    cfg.model.init_args.ckpt_path = str(cfg.ckpt_path)
+    instances = parser.instantiate(cfg)
+    vsunet = instances.model
+    vsunet.eval().to(device)
+    click.echo(f"[{position_name}] Loaded checkpoint: {cfg.ckpt_path}")
+
+    # Test-time augmentation: reuse VisCy's rotation-TTA helper, which is
+    # correct for non-square FOVs. Defaults follow the config's model flags.
+    use_tta = bool(getattr(cfg.model.init_args, "test_time_augmentations", False))
+    tta_type = getattr(cfg.model.init_args, "tta_type", "mean")
+    reduction = "median" if tta_type == "median" else "mean"
+    if use_tta:
+        predictor = AugmentedPredictionVSUNet.with_rotation_tta(
+            vsunet.model, reduction=reduction
+        )
+        click.echo(
+            f"[{position_name}] Test-time augmentation: on ({reduction} over 4 rotations)"
+        )
+    else:
+        predictor = AugmentedPredictionVSUNet(model=vsunet.model)
+        click.echo(f"[{position_name}] Test-time augmentation: off")
+    predictor = predictor.eval().to(device)
+
+    # Normalization: reuse the configured NormalizeSampled transforms directly.
+    normalize = Compose(instances.data.normalizations)
+
+    source_channel = cfg.data.init_args.source_channel
+    source_channel = source_channel[0] if isinstance(source_channel, list) else source_channel
+    target_channels = cfg.data.init_args.target_channel
+    target_channels = (
+        [target_channels] if isinstance(target_channels, str) else target_channels
+    )
+    out_channel = len(target_channels)
+    step = cfg.sliding_window_step
+
+    with open_ome_zarr(str(input_position_path), mode="r") as input_dataset:
+        T, _, Z, Y, X = input_dataset.data.shape
+        channel_index = input_dataset.channel_names.index(source_channel)
+        # Precomputed statistics written by `viscy preprocess`.
+        norm_meta = read_norm_meta(input_dataset)
+        if norm_meta is None or source_channel not in norm_meta:
+            raise RuntimeError(
+                f"Normalization statistics for channel '{source_channel}' were not "
+                f"found in the input store. Run `viscy preprocess` on the input "
+                f"dataset before virtual staining."
+            )
+
+        # Depth of the Z-window fed to the model. In VisCy's own
+        # `predict_sliding_windows`, `model.out_stack_depth` is the input window
+        # depth: each window is `x[:, :, start:start + out_stack_depth]`. We
+        # mirror that here purely to log the window count (the value matches
+        # `len(range(0, Z - out_stack_depth + 1, step))` exactly). "?" when the
+        # model exposes no such attribute, and 0 when the volume is too shallow
+        # for even one window (predict_sliding_windows then raises).
+        z_window_depth = getattr(vsunet.model, "out_stack_depth", None)
+        n_windows = max(0, (Z - z_window_depth) // step + 1) if z_window_depth else "?"
+        click.echo(
+            f"[{position_name}] {T} timepoints, volume (Z,Y,X)=({Z},{Y},{X}), "
+            f"'{source_channel}' -> {target_channels}, "
+            f"{n_windows} sliding windows/timepoint (step={step})"
+        )
+
+        with open_ome_zarr(str(output_position_path), mode="r+") as output_dataset:
+            for t in range(T):
+                t_start = time.perf_counter()
+                # (1, 1, Z, Y, X) source volume for this timepoint
+                volume = np.asarray(
+                    input_dataset.data[t : t + 1, channel_index : channel_index + 1]
+                )
+                source = torch.from_numpy(volume).float().to(device)
+                # Apply VisCy's configured normalization via a sample dict.
+                sample = normalize({source_channel: source, "norm_meta": norm_meta})
+                source = sample[source_channel]
+                with torch.inference_mode():
+                    prediction = predictor.predict_sliding_windows(
+                        source, out_channel=out_channel, step=step
+                    )
+                output_dataset.data[t] = prediction[0].cpu().numpy()
+                click.echo(
+                    f"[{position_name}] timepoint {t + 1}/{T} done "
+                    f"({time.perf_counter() - t_start:.1f}s)"
+                )
+
+    click.echo(
+        f"[{position_name}] Completed {T} timepoints in "
+        f"{time.perf_counter() - position_start:.1f}s -> {output_position_path}"
+    )
 
-        if not nested_fov_path.exists():
-            print(f"Skipping missing: {nested_fov_path}")
-            continue
 
-        dest_path = output_dirpath / row / col / pos
+def _init_output_plate(
+    input_position_dirpaths: list[Path],
+    output_dirpath: Path,
+    target_channels: list[str],
+    output_ome_zarr_version: str | None = None,
+) -> tuple[int, int, int, int, int]:
+    """Create (or extend) the empty virtual-stain output plate.
 
-        if dest_path.exists():
-            shutil.rmtree(dest_path)
+    The output mirrors the input geometry but contains only the predicted
+    virtual-stain channels. ``create_empty_plate`` is idempotent, so this is
+    safe to call from both the orchestrator and per-position runs.
 
-        print(f"Moving {nested_fov_path} → {dest_path}")
-        shutil.move(str(nested_fov_path), str(dest_path))
+    ``output_ome_zarr_version`` dictates the output store's OME-Zarr version;
+    when None the input store's version is preserved.
 
-    # Optionally remove the full temp zarr folder if it's now empty
-    if cleanup:
-        try:
-            shutil.rmtree(temp_dir)
-        except Exception as e:
-            print(f"Could not remove {temp_dir}: {e}")
+    Returns the input ``(T, C, Z, Y, X)`` shape.
+    """
+    with open_ome_zarr(str(input_position_dirpaths[0]), mode="r") as input_dataset:
+        input_shape = input_dataset.data.shape
+        scale = input_dataset.scale
+    T, C, Z, Y, X = input_shape
+
+    create_empty_plate(
+        store_path=output_dirpath,
+        position_keys=[Path(p).parts[-3:] for p in input_position_dirpaths],
+        channel_names=target_channels,
+        shape=(T, len(target_channels), Z, Y, X),
+        scale=scale,
+        version=resolve_ome_zarr_version(input_position_dirpaths[0], output_ome_zarr_version),
+    )
 
-    print(f"Combined all FOVs into {output_dirpath}")
+    return (T, C, Z, Y, X)
 
 
 def virtual_stain(
     input_position_dirpaths: list[str],
+    config_filepath: Path,
     output_dirpath: str,
-    predict_config_filepath: str,
-    path_viscy_env: str,
-    preprocess_config_filepath: str = None,
-    sbatch_filepath_preprocess: str = None,
-    sbatch_filepath_predict: str = None,
-    run_mode: str = "all",
-    num_processes: int = 32,
-    local: bool = False,
+    sbatch_filepath: str = None,
+    cluster: str = "slurm",
     monitor: bool = True,
-    verbose: bool = True,
+    init_only: bool = False,
 ):
-    """
-    Run VisCy virtual stain on a plate.
+    """Run cytoland virtual staining on a plate, one GPU job per position.
+
+    Opens the input store, creates an output store containing only the
+    predicted virtual-stain channels, and dispatches one job per position. Each
+    job runs ``virtual_stain_position``, which loops over timepoints on a single
+    GPU.
 
     Parameters
     ----------
-    input_position_dirpaths : List[str]
-        List of paths to the input position directories.
+    input_position_dirpaths : list[str]
+        Input position directory paths.
+    config_filepath : Path
+        Path to the VisCy-style predict config.
     output_dirpath : str
-        Path to the output directory.
-    predict_config_filepath : str
-        Path to the VisCy predict config file.
-    preprocess_config_filepath : str
-        Path to the VisCy preprocess config file.
-    path_viscy_env : str
-        Path to the VisCy environment.
-    sbatch_filepath_preprocess : str
-        Path to the VisCy preprocess sbatch file.
-    sbatch_filepath_predict : str
-        Path to the VisCy predict sbatch file.
-    run_mode : str
-        Which VisCy stage(s) to run.
-    num_processes : int
-        Number of processes to use.
-    local : bool
-        Whether to run locally.
-    monitor : bool
-
+        Output plate path.
+    sbatch_filepath : str, optional
+        SBATCH file overriding default SLURM parameters.
+    cluster : str, optional
+        Execution cluster: 'slurm' submits to a Slurm cluster, 'local' runs jobs
+        as subprocesses on this machine, 'debug' runs jobs in-process in the
+        foreground.
+    monitor : bool, optional
+        Monitor submitted jobs.
+    init_only : bool, optional
+        Only initialize the output store and exit; skip per-position processing.
     """
+    # The 'local' cluster launches every position as a concurrent subprocess
+    # (submitit's LocalExecutor ignores `slurm_array_parallelism`, so there is no
+    # concurrency cap) and biahub does not pin per-job GPUs, so all jobs target
+    # cuda:0. With more than one position that oversubscribes a single GPU. Use
+    # 'slurm' for multi-GPU parallelism or 'debug' to run positions one at a time.
+    if cluster == "local" and len(input_position_dirpaths) > 1:
+        raise ValueError(
+            f"cluster='local' cannot run {len(input_position_dirpaths)} positions: "
+            "local jobs run concurrently without a parallelism cap and all target "
+            "the same GPU. Use cluster='slurm' for multi-GPU parallelism, or "
+            "cluster='debug' to run positions sequentially on this machine."
+        )
+
     output_dirpath = Path(output_dirpath)
+    config_filepath = Path(config_filepath)
     slurm_out_path = output_dirpath.parent / "slurm_output"
 
-    cluster = get_submitit_cluster(local)
-
-    job_ids_preprocess = []
-    slurm_dependency = None
-    path_viscy_env = Path(path_viscy_env)
-
-    if run_mode in ["all", "preprocess"]:
-        slurm_args_preprocess = {
-            "slurm_job_name": "VS_preprocess",
-            "slurm_mem_per_cpu": "8G",
-            "slurm_cpus_per_task": num_processes,
-            "slurm_array_parallelism": 1,
-            "slurm_time": 8 * 60,
-            "slurm_partition": "cpu",
-        }
-
-        if sbatch_filepath_preprocess:
-            slurm_args_preprocess.update(sbatch_to_submitit(sbatch_filepath_preprocess))
-
-        executor = submitit.AutoExecutor(folder=slurm_out_path / "preprocess", cluster=cluster)
-        executor.update_parameters(**slurm_args_preprocess)
-        click.echo(f"Submitting preprocess job with: {slurm_args_preprocess}")
-
-        plate_path = Path(input_position_dirpaths[0]).parents[2]
-        with executor.batch():
-            job = executor.submit(
-                run_viscy_preprocess,
-                data_path=str(plate_path),
-                num_workers=num_processes,
-                config_file=preprocess_config_filepath,
-                path_viscy_env=path_viscy_env,
-                verbose=verbose,
-            )
-            job_ids_preprocess.append(job)
-
-        job_ids = [
-            job.job_id for job in job_ids_preprocess
-        ]  # Access job IDs after batch submission
-
-        log_path = Path(slurm_out_path / "preprocess" / "submitit_jobs_ids.log")
-        log_path.parent.mkdir(parents=True, exist_ok=True)
-        with log_path.open("w") as log_file:
-            log_file.write("\n".join(job_ids))
-
-        slurm_dependency = f"afterok:{job.job_id}"
-
-    job_ids_predict = []
-    job_ids_combine = []
-
-    if run_mode in ["all", "predict"]:
-        slurm_args_predict = {
-            "slurm_job_name": "VS_predict",
-            "slurm_mem_per_cpu": "16G",
-            "slurm_cpus_per_task": 32,
-            "slurm_array_parallelism": 100,
-            "slurm_time": 8 * 60,
-            "slurm_partition": "gpu",
-            "slurm_gres": "gpu:1",
-            "slurm_constraint": "a100|a6000|a40",
-            "slurm_use_srun": False,
-        }
-
-        if slurm_dependency:
-            slurm_args_predict["slurm_dependency"] = slurm_dependency
-
-        if sbatch_filepath_predict:
-            slurm_args_predict.update(sbatch_to_submitit(sbatch_filepath_predict))
-
-        executor = submitit.AutoExecutor(folder=slurm_out_path / "predict", cluster=cluster)
-        executor.update_parameters(**slurm_args_predict)
-        click.echo(f"Submitting predict jobs with: {slurm_args_predict}")
-        config_file = str(Path(predict_config_filepath).resolve())
-        fovs = []
-
-        with executor.batch():
-            for input_position_path in input_position_dirpaths:
-                fov = Path(*input_position_path.parts[-3:])
-                fovs.append(fov)
-                log_dir = (output_dirpath.parent / "logs" / "_".join(fov.parts)).resolve()
-                os.makedirs(log_dir, exist_ok=True)
-                data_path = str(Path(input_position_path).resolve())
-
-                output_fov_path = (
-                    output_dirpath.parent / "temp" / f"{'_'.join(fov.parts)}.zarr"
-                )
-                output_store = str(Path(output_fov_path).resolve())
-
-                job = executor.submit(
-                    run_viscy_predict,
-                    data_path=data_path,
-                    config_file=config_file,
-                    output_store=output_store,
-                    log_dir=log_dir,
-                    path_viscy_env=path_viscy_env,
-                    verbose=verbose,
-                )
-                job_ids_predict.append(job)
-
-        job_ids = [
-            job.job_id for job in job_ids_predict
-        ]  # Access job IDs after batch submission
-
-        log_path = Path(slurm_out_path / "predict" / "submitit_jobs_ids.log")
-        log_path.parent.mkdir(parents=True, exist_ok=True)
-        with log_path.open("w") as log_file:
-            log_file.write("\n".join(job_ids))
-
-        with open_ome_zarr(input_position_dirpaths[0]) as dataset:
-            T, C, Z, Y, X = dataset.data.shape
-            channel_names = dataset.channel_names
-            scale = dataset.scale
-
-        output_metadata = {
-            "shape": (T, len(channel_names), Z, Y, X),
-            "chunks": None,
-            "scale": scale,
-            "channel_names": channel_names,
-            "dtype": np.float32,
-        }
-
-        create_empty_plate(
-            store_path=output_dirpath,
-            position_keys=[p.parts[-3:] for p in input_position_dirpaths],
-            **output_metadata,
-        )
+    # Validate the config against VisCy's schema up front (using the first
+    # position for data_path) and read the predicted channel names.
+    _, cfg = load_predict_config(config_filepath, input_position_dirpaths[0])
+    target_channels = cfg.data.init_args.target_channel
+    target_channels = (
+        [target_channels] if isinstance(target_channels, str) else target_channels
+    )
 
-        slurm_args_combine = {
-            "slurm_job_name": "VS_combine",
-            "slurm_mem_per_cpu": "8G",
-            "slurm_cpus_per_task": num_processes,
-            "slurm_array_parallelism": 1,
-            "slurm_time": 8 * 60,
-            "slurm_partition": "cpu",
-            "slurm_dependency": f"afterok:{':'.join([str(job.job_id) for job in job_ids_predict])}",
-        }
-
-        executor = submitit.AutoExecutor(folder=slurm_out_path / "combine", cluster=cluster)
-        executor.update_parameters(**slurm_args_combine)
-        click.echo(f"Submitting combine job with: {slurm_args_combine}")
-
-        plate_path = Path(input_position_dirpaths[0]).parents[2]
-
-        with executor.batch():
-            job = executor.submit(
-                combine_fov_zarrs_to_plate,
-                fovs=fovs,
-                temp_dir=output_dirpath.parent / "temp",
-                output_dirpath=output_dirpath,
-                cleanup=True,
+    input_shape = _init_output_plate(
+        input_position_dirpaths,
+        output_dirpath,
+        target_channels,
+        cfg.output_ome_zarr_version,
+    )
+
+    # Timepoints are processed sequentially on a single GPU, so resource needs
+    # are independent of dataset size.
+    num_cpus, gb_ram = 16, 64
+    click.echo(f"RESOURCES:{num_cpus} {gb_ram}")
+
+    if init_only:
+        click.echo(f"Initialized {output_dirpath} ({len(input_position_dirpaths)} positions)")
+        return
+
+    output_position_paths = utils.get_output_paths(input_position_dirpaths, output_dirpath)
+
+    T, Z = input_shape[0], input_shape[2]
+    # Generous wall-clock budget (minutes), assuming median TTA (4 rotations).
+    # Each timepoint runs ~Z sliding windows along Z. Measured ~1.4 s/window
+    # with TTA on this model; budget ~5 s/window (~3-4x margin for slower GPUs,
+    # larger FOVs, and compute-bound runs) with a 60-minute floor.
+    seconds_per_window = 5
+    slurm_time = int(np.ceil(max(60, T * Z * seconds_per_window / 60)))
+    # Prepare SLURM arguments
+    slurm_args = {
+        "slurm_job_name": "virtual-stain",
+        "slurm_gres": "gpu:1",
+        "slurm_mem_per_cpu": f"{gb_ram}G",
+        "slurm_cpus_per_task": num_cpus,
+        "slurm_array_parallelism": 20,  # process up to 20 positions at a time
+        "slurm_time": slurm_time,
+        "slurm_partition": "gpu",
+    }
+
+    # Override defaults if sbatch_filepath is provided
+    if sbatch_filepath:
+        slurm_args.update(sbatch_to_submitit(sbatch_filepath))
+
+    resolved_cluster = get_submitit_cluster(cluster=cluster)
+    click.echo(f"Preparing jobs on cluster='{resolved_cluster}': {slurm_args}")
+    executor = submitit.AutoExecutor(folder=slurm_out_path, cluster=resolved_cluster)
+    executor.update_parameters(**slurm_args)
+
+    click.echo("Submitting jobs...")
+    jobs = []
+    with submitit.helpers.clean_env(), executor.batch():
+        for input_position_path, output_position_path in zip(
+            input_position_dirpaths, output_position_paths, strict=True
+        ):
+            jobs.append(
+                executor.submit(
+                    virtual_stain_position,
+                    config_filepath,
+                    input_position_path,
+                    output_position_path,
+                )
             )
-            job_ids_combine.append(job)
 
-        job_ids = [
-            job.job_id for job in job_ids_combine
-        ]  # Access job IDs after batch submission
+    job_ids = [job.job_id for job in jobs]  # Access job IDs after batch submission
+
+    slurm_out_path.mkdir(parents=True, exist_ok=True)
+    log_path = Path(slurm_out_path / "submitit_jobs_ids.log")
+    with log_path.open("w") as log_file:
+        log_file.write("\n".join(job_ids))
 
-        log_path = Path(slurm_out_path / "combine" / "submitit_jobs_ids.log")
-        log_path.parent.mkdir(parents=True, exist_ok=True)
-        with log_path.open("w") as log_file:
-            log_file.write("\n".join(job_ids))
+    # submitit's DebugExecutor is lazy: .submit() wraps the callable in a
+    # DebugJob but execution only happens when .wait()/.done()/.result() is
+    # called. Run each one in the foreground and stream progress; monitor's
+    # async polling UI is pointless against synchronous in-process jobs.
+    if resolved_cluster == "debug":
+        for job, path in zip(jobs, input_position_dirpaths, strict=True):
+            job.wait()
+            click.echo(f"Virtual staining complete: {path}")
+        return
 
     if monitor:
-        job_ids = [
-            job.job_id for job in job_ids_predict + job_ids_preprocess + job_ids_combine
-        ]
-        monitor_jobs(job_ids, input_position_dirpaths)
+        monitor_jobs(jobs, input_position_dirpaths)
 
 
 @click.command("virtual-stain")
 @input_position_dirpaths()
+@config_filepath()
 @output_dirpath()
-@sbatch_filepath_preprocess()
-@sbatch_filepath_predict()
-@num_processes()
-@local()
+@sbatch_filepath()
+@cluster()
 @monitor()
-@click.option("--verbose", is_flag=True, default=False, help="Verbose output.")
-@click.option(
-    "--path-viscy-env",
-    required=True,
-    help="Conda environment with VisCy installed.",
-)
-@click.option(
-    "--preprocess-config-filepath",
-    type=str,
-    help="Path to the VisCy preprocess config file.",
-)
-@click.option(
-    "--predict-config-filepath",
-    type=str,
-    help="Path to the VisCy predict config file.",
-)
-@click.option(
-    "--run-mode",
-    type=click.Choice(["all", "preprocess", "predict"]),
-    default="all",
-    help="Which VisCy stage(s) to run.",
-)
+@init_only()
 def virtual_stain_cli(
     input_position_dirpaths: list[str],
+    config_filepath: Path,
     output_dirpath: str,
-    predict_config_filepath: str,
-    path_viscy_env: str,
-    preprocess_config_filepath: str = None,
-    run_mode: str = "all",
-    num_processes: int = 32,
-    sbatch_filepath_preprocess: str = None,
-    sbatch_filepath_predict: str = None,
-    local: bool = False,
-    monitor: bool = True,
-    verbose: bool = True,
+    sbatch_filepath: str = None,
+    cluster: str = "slurm",
+    monitor: bool = False,
+    init_only: bool = False,
 ):
-    """Run VisCy virtual staining on a zarr plate from dedicated python environment.
-
-    >>> biahub virtual-stain \
-        --input-position-dirpaths path.zarr/*/*/* \
-        --output-dirpath output.zarr \
-        --predict-config-filepath predict.yml \
-        --preprocess-config-filepath preprocess.yml \
-        --path-viscy-env /path/to/viscy/env \
-        --run-mode all
-    """
+    """Virtually stain a label-free dataset using a cytoland (VisCy) model.
+
+    Runs cytoland's ``predict_sliding_windows`` per position, looping over
+    timepoints on a single GPU. The config is a ``viscy predict`` YAML and is
+    validated against VisCy's own model/data classes.
+
+    \b
+    SLURM fan-out of positions across a whole plate:
+    >>> biahub virtual-stain -i ./input.zarr/*/*/* -c ./virtual_stain_params.yml -o ./output.zarr
+
+    \b
+    Initialize the output plate only (e.g. before running per-position Nextflow workers):
+    >>> biahub virtual-stain --init -i ./input.zarr/*/*/* -c ./virtual_stain_params.yml -o ./output.zarr
+
+    \b
+    In-process run of a single position (e.g. from a Nextflow worker):
+    >>> biahub virtual-stain --cluster debug -i ./input.zarr/A/1/0 -c ./virtual_stain_params.yml -o ./output.zarr
+    """  # noqa: D301
     virtual_stain(
         input_position_dirpaths=input_position_dirpaths,
+        config_filepath=config_filepath,
         output_dirpath=output_dirpath,
-        predict_config_filepath=predict_config_filepath,
-        preprocess_config_filepath=preprocess_config_filepath,
-        sbatch_filepath_preprocess=sbatch_filepath_preprocess,
-        sbatch_filepath_predict=sbatch_filepath_predict,
-        path_viscy_env=path_viscy_env,
-        run_mode=run_mode,
-        num_processes=num_processes,
-        local=local,
+        sbatch_filepath=sbatch_filepath,
+        cluster=cluster,
         monitor=monitor,
-        verbose=verbose,
+        init_only=init_only,
     )
 
 
diff --git a/pyproject.toml b/pyproject.toml
index 1a61abe2..39a7bdbb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,10 +52,7 @@ track = ["ultrack>=0.7"]
 
 gui = ["napari[pyqt6]", "PyQt6>=6.5,<6.8", "napari-animation"]
 
-# TODO: resolved to v0.3.2 which is not compatible with iohub 0.3.0a7
-# bring bach once we sort out iohub dependencies
-# stain = ["viscy"]
-stain = []
+stain = ["cytoland"]
 
 build = ["build", "twine"]
 
@@ -84,6 +81,9 @@ biahub = "biahub.cli.main:cli"
 index-strategy = "unsafe-best-match"
 
 [tool.uv.sources]
+# Pin cytoland to the commit from VisCy PR #465 (non-square TTA and helpers).
+# Revert to the PyPI release once that PR is merged and published.
+cytoland = { git = "https://github.com/mehta-lab/VisCy", subdirectory = "applications/cytoland", rev = "7fb3bc1e78c0e0bd26e1845caba860f76223ba02" }
 # Pin iohub to the branch that stores per-step provenance as top-level
 # zattrs keys (e.g. biahub-flat_field, biahub-deskew). Revert to the PyPI
 # release once those changes are merged and published.
diff --git a/settings/example_virtual_stain_settings.yml b/settings/example_virtual_stain_settings.yml
new file mode 100644
index 00000000..206763aa
--- /dev/null
+++ b/settings/example_virtual_stain_settings.yml
@@ -0,0 +1,44 @@
+# Virtual staining (cytoland / VisCy) predict config for `biahub virtual-stain`.
+# This is a `viscy predict` config and is validated against VisCy's own
+# VSUNet / HCSDataModule classes. `data.init_args.data_path` is injected by
+# biahub per position, so it is omitted here.
+model:
+  class_path: cytoland.engine.VSUNet
+  init_args:
+    architecture: fcmae
+    model_config:
+      in_channels: 1
+      out_channels: 2
+      in_stack_depth: 15
+      encoder_blocks: [3, 3, 9, 3]
+      dims: [96, 192, 384, 768]
+      decoder_conv_blocks: 2
+      stem_kernel_size: [5, 4, 4]
+      pretraining: false
+    test_time_augmentations: true
+    tta_type: median
+
+data:
+  class_path: viscy_data.hcs.HCSDataModule
+  init_args:
+    source_channel: Phase3D
+    target_channel: [nuclei, membrane]
+    z_window_size: 15  # must match model in_stack_depth
+    # No effect on biahub: predict_sliding_windows processes one z-window at a
+    # time. Kept for parity with `viscy predict` configs.
+    batch_size: 1
+    normalizations:
+      - class_path: viscy_transforms.NormalizeSampled
+        init_args:
+          keys: [Phase3D]
+          level: fov_statistics
+          subtrahend: median
+          divisor: iqr
+
+ckpt_path: /hpc/projects/comp.micro/virtual_staining/models/fcmae-cyto3d-sensor/vscyto3d-logs/hek-a549-ipsc-finetune/checkpoints/epoch=83-step=14532-loss=0.492.ckpt
+
+# Optional biahub orchestration knobs (defaults shown).
+sliding_window_step: 1  # Z step for the sliding window; 1 = maximum overlap.
+device: cuda  # torch device for inference.
+# Output OME-Zarr version: null preserves the input store's version; "0.4" or "0.5" overrides.
+output_ome_zarr_version: null
diff --git a/tests/test_example_settings.py b/tests/test_example_settings.py
index f3e48fef..e0f12453 100644
--- a/tests/test_example_settings.py
+++ b/tests/test_example_settings.py
@@ -55,6 +55,12 @@
     ("example_flat_field_settings.yml", FlatFieldCorrectionSettings),
 ]
 
+# Example configs validated against VisCy's own classes via jsonargparse rather
+# than a biahub pydantic Settings model (virtual staining keeps no model schema).
+jsonargparse_settings_files = [
+    "example_virtual_stain_settings.yml",
+]
+
 try:
     import cellpose  # noqa: F401
 
@@ -62,17 +68,38 @@
 except ImportError:
     cellpose_available = False
 
+try:
+    import cytoland  # noqa: F401
+
+    cytoland_available = True
+except ImportError:
+    cytoland_available = False
+
 
 def test_all_example_settings_tested():
     num_settings_files = len(
         list(settings_files_dir.glob("*.yml")) + list(settings_files_dir.glob("*.yaml"))
     )
-    assert num_settings_files == len(example_settings_params), (
+    num_tested = len(example_settings_params) + len(jsonargparse_settings_files)
+    assert num_settings_files == num_tested, (
         "Not all example settings files are tested. "
-        f"Found {num_settings_files} files, but {len(example_settings_params)} are tested in test_example_settings."
+        f"Found {num_settings_files} files, but {num_tested} are tested in test_example_settings."
     )
 
 
+@pytest.mark.parametrize("path", jsonargparse_settings_files)
+def test_example_jsonargparse_settings(path):
+    # These configs are validated against VisCy's VSUNet/HCSDataModule classes,
+    # which require the optional `stain` extra (cytoland).
+    if not cytoland_available:
+        pytest.skip("cytoland not installed; skipping VisCy-config validation.")
+
+    from biahub.virtual_stain import load_predict_config
+
+    # data_path is injected per position at runtime; any placeholder validates.
+    load_predict_config(settings_files_dir / path, Path("/placeholder.zarr/A/1/0"))
+
+
 @pytest.mark.parametrize("path,settings_cls", example_settings_params)
 def test_example_settings(path, settings_cls):
     # Skip test if cellpose isn't installed and we're testing SegmentationSettings
diff --git a/uv.lock b/uv.lock
index 26a5ec62..d50bb4ac 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,18 +2,30 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.14' and sys_platform == 'win32'",
-    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.14' and sys_platform == 'darwin'",
-    "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version == '3.13.*' and sys_platform == 'win32'",
-    "python_full_version < '3.13' and sys_platform == 'win32'",
-    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
-    "python_full_version < '3.13' and sys_platform == 'emscripten'",
-    "python_full_version == '3.13.*' and sys_platform == 'darwin'",
-    "python_full_version < '3.13' and sys_platform == 'darwin'",
-    "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'darwin'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'darwin'",
+    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'darwin'",
+    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'darwin'",
+    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
 [[package]]
@@ -179,7 +191,7 @@ wheels = [
 
 [[package]]
 name = "antspyx"
-version = "0.6.3"
+version = "0.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "matplotlib" },
@@ -193,16 +205,16 @@ dependencies = [
     { name = "statsmodels" },
     { name = "webcolors" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/8b/50/a421ddf4f30ab6d47bd1867c4eb2680aadea94533c011e6ef6451f208054/antspyx-0.6.3.tar.gz", hash = "sha256:48d1f5b6b2e8a90f6f66f14cdbdd4c37e20268f5db84cb9ac99256ecb9c72386", size = 7196895, upload-time = "2026-02-24T01:46:00.875Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/e6/72ae72bf5e217237026bd4e24b3836959982a328d9c444b26fd53997815d/antspyx-0.6.1.tar.gz", hash = "sha256:fd3f3a1749fb5a22b5ef3e73be17bcf2d8cf30636796abe04b1ea356d449a193", size = 7185950, upload-time = "2025-06-25T22:58:18.703Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c1/a4/12b5855418e5afddc588f645742c26cd2d6805cee2f745679eb06c2afc00/antspyx-0.6.3-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:e378329089f622c72eacc47e4e2c18503d0f3fbf2cc39ccb9df65880c0a7846f", size = 16637863, upload-time = "2026-02-24T01:45:37.915Z" },
-    { url = "https://files.pythonhosted.org/packages/be/e4/11956ea04c9653f5d99f7178a5e06daa7fc43b7a08a3a274879d49eeb302/antspyx-0.6.3-cp312-cp312-macosx_15_0_x86_64.whl", hash = "sha256:68c1ee777f13398806366c339f460065ba65bcd9cc7c1daba7cfb8663e63c8f4", size = 18617303, upload-time = "2026-02-24T01:45:40.413Z" },
-    { url = "https://files.pythonhosted.org/packages/ab/c7/e942d86dab13c19b1de024cb8891c65ef59c24985995805f648e051b4137/antspyx-0.6.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b0acddaed4b52a5374a032e5cefe483852196bc727373e18081104d6ed5faf2a", size = 22443702, upload-time = "2026-02-24T01:45:42.718Z" },
-    { url = "https://files.pythonhosted.org/packages/a0/c7/27d9e70bddc6489f3852227748386ca293e8fa50bbee5b49ec1329f13289/antspyx-0.6.3-cp312-cp312-win_amd64.whl", hash = "sha256:a3011711704037202fcabb8da7da917fc667c99f54a694f0c1f58251e67b1a4f", size = 8786793, upload-time = "2026-02-24T01:45:45.124Z" },
-    { url = "https://files.pythonhosted.org/packages/44/53/3ebd21adb38422f833d6406e03ce64c3dc09b2e7dceade3a5448334e90f0/antspyx-0.6.3-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:0c9ec8fcee72d7c7fe0d820dbfc9d7f929db3306037be27f24b52e30ae1ccbc0", size = 16637911, upload-time = "2026-02-24T01:45:47.481Z" },
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 [[package]]
@@ -303,6 +315,7 @@ dependencies = [
 all = [
     { name = "build" },
     { name = "cellpose" },
+    { name = "cytoland" },
     { name = "napari", extra = ["pyqt6"] },
     { name = "napari-animation" },
     { name = "pyqt6" },
@@ -325,6 +338,9 @@ gui = [
 segment = [
     { name = "cellpose" },
 ]
+stain = [
+    { name = "cytoland" },
+]
 track = [
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@@ -345,6 +361,8 @@ requires-dist = [
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     { name = "cellpose", marker = "extra == 'segment'" },
     { name = "click" },
+    { name = "cytoland", marker = "extra == 'all'", git = "https://github.com/mehta-lab/VisCy?subdirectory=applications%2Fcytoland&rev=7fb3bc1e78c0e0bd26e1845caba860f76223ba02" },
+    { name = "cytoland", marker = "extra == 'stain'", git = "https://github.com/mehta-lab/VisCy?subdirectory=applications%2Fcytoland&rev=7fb3bc1e78c0e0bd26e1845caba860f76223ba02" },
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+    { name = "lightning" },
+    { name = "monai" },
+    { name = "torchmetrics" },
+    { name = "viscy-data" },
+    { name = "viscy-models" },
+    { name = "viscy-transforms" },
+    { name = "viscy-utils" },
+]
+
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 name = "dask"
 version = "2026.3.0"
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 [package.optional-dependencies]
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+wheels = [
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+]
+
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 name = "typing-extensions"
 version = "4.15.0"
@@ -5091,6 +5650,70 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/4b/eb/03bfb1299d4c4510329e470f13f9a4ce793df7fcb5a2fd3510f911066f61/virtualenv-21.3.0-py3-none-any.whl", hash = "sha256:4d28ee41f6d9ec8f1f00cd472b9ffbcedda1b3d3b9a575b5c94a2d004fd51bd7", size = 7594690, upload-time = "2026-04-27T17:05:55.468Z" },
 ]
 
+[[package]]
+name = "viscy-data"
+version = "0.0.0.post211.dev0+7fb3bc1"
+source = { git = "https://github.com/mehta-lab/VisCy?subdirectory=packages%2Fviscy-data&rev=7fb3bc1e78c0e0bd26e1845caba860f76223ba02#7fb3bc1e78c0e0bd26e1845caba860f76223ba02" }
+dependencies = [
+    { name = "imageio" },
+    { name = "iohub" },
+    { name = "lightning" },
+    { name = "monai" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "pyarrow" },
+    { name = "pydantic" },
+    { name = "torch" },
+    { name = "tqdm" },
+    { name = "zarr" },
+]
+
+[[package]]
+name = "viscy-models"
+version = "0.0.0.post211.dev0+7fb3bc1"
+source = { git = "https://github.com/mehta-lab/VisCy?subdirectory=packages%2Fviscy-models&rev=7fb3bc1e78c0e0bd26e1845caba860f76223ba02#7fb3bc1e78c0e0bd26e1845caba860f76223ba02" }
+dependencies = [
+    { name = "monai" },
+    { name = "numpy" },
+    { name = "pytorch-metric-learning" },
+    { name = "timm" },
+    { name = "torch" },
+    { name = "transformers" },
+]
+
+[[package]]
+name = "viscy-transforms"
+version = "0.0.0.post211.dev0+7fb3bc1"
+source = { git = "https://github.com/mehta-lab/VisCy?subdirectory=packages%2Fviscy-transforms&rev=7fb3bc1e78c0e0bd26e1845caba860f76223ba02#7fb3bc1e78c0e0bd26e1845caba860f76223ba02" }
+dependencies = [
+    { name = "kornia" },
+    { name = "monai" },
+    { name = "numpy" },
+    { name = "torch" },
+]
+
+[[package]]
+name = "viscy-utils"
+version = "0.0.0.post211.dev0+7fb3bc1"
+source = { git = "https://github.com/mehta-lab/VisCy?subdirectory=packages%2Fviscy-utils&rev=7fb3bc1e78c0e0bd26e1845caba860f76223ba02#7fb3bc1e78c0e0bd26e1845caba860f76223ba02" }
+dependencies = [
+    { name = "iohub" },
+    { name = "jsonargparse", extra = ["signatures"] },
+    { name = "lightning" },
+    { name = "matplotlib" },
+    { name = "numpy" },
+    { name = "onnx" },
+    { name = "onnxscript" },
+    { name = "pyyaml" },
+    { name = "scikit-image" },
+    { name = "scipy" },
+    { name = "tensorboard" },
+    { name = "tensorstore" },
+    { name = "torch" },
+    { name = "viscy-data" },
+    { name = "xarray" },
+]
+
 [[package]]
 name = "vispy"
 version = "0.16.1"