Run a Nerfstudio workflow

Documentation Index

Fetch the complete documentation index at: https://docs.cyberun.cloud/llms.txt

Use this file to discover all available pages before exploring further.

The Nerfstudio wizard in Cloud builds a workflow for a 3D Gaussian splatting pipeline (splatfacto or splatfacto-big) without hand-editing JSON. You provide input captures and parameters; the wizard outputs a workflow you can save and run. The wizard must appear on the workflow create flow. If not, the Nerfstudio isn’t enabled on this deployment (contact your team admin), or no agent in your team carries the nerfstudio label.

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Before you start

• A capture you want to reconstruct. Three input kinds are supported:

  Input kind	What it is
  images_zip	A ZIP of still images of the subject.
  video	A video file that will be sampled into frames.
  processed_dataset_zip	A pre-processed Nerfstudio dataset ZIP.

• A team agent carrying the nerfstudio label (and the gpu label, of course).

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Build the workflow

1. Open Workflows in the sidebar.
2. Click Create workflow and pick Nerfstudio wizard.
3. Fill in the wizard:

   Field	Notes
   Input kind	images_zip, video, or processed_dataset_zip.
   Training method	splatfacto (faster, lower quality) or splatfacto-big (slower, higher quality).
   File parameter key	The name of the file parameter callers will supply. Defaults to something sensible.
   File parameter label	The human-readable label shown on the run form.
   Max iterations	Training iterations, 1000–100000.
   Camera type	Free text. Leave blank to let the runtime pick.
   Run process_data	Toggle. Enables Nerfstudio’s pre-processing step.
   Export Gaussian splat	Toggle. Adds the .ply export step at the end.

4. Click Save to create the workflow.

The wizard shows the generated workflow spec inline (in a collapsible preview) before saving so you can review it.

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Run

The new workflow appears in the Workflows list. From the detail page:

1. Click Run.
2. Upload the input (images ZIP, video, or processed dataset).
3. Submit.

The task moves through pending → waiting → queued → running → completed. Long-running training tasks emit progress events with step counts that the task detail page renders inline.

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Output

When the task succeeds, the output panel shows:

• A primary artifact (the splat .ply or the video, depending on toggles).
• A storage key referencing the rest of the output (config, file list, manifest).

You can re-run the workflow with new inputs or different parameters by submitting another task.

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Related

• Workflows — concept.
• Tasks — lifecycle and progress events.
• Agents — what carries the nerfstudio label.