List

Define a list of items — or a multi-column typed table — for batch iteration.

Overview

The List node is a fan-out source: every downstream node connected to it runs once per row in the list. It is a data node — it makes no API call, produces no job, and costs 0 credits.

By default it is a simple single-column list of text items (“Items”) — paste or add one value per row, and each value is emitted to downstream nodes in turn. When you need more than one variable per iteration, the same node grows into a multi-column typed table: connect a producer to the node’s bottom-left ”+” handle and a new column is added. Each column is typed and gets its own input handle (to receive values from upstream) and output handle (to feed a downstream node).

The config panel adapts to the column count: it shows the single-column List editor at one column and the multi-column Table editor once you have more than one. The node’s view mode (list / gallery / packed) is chosen automatically from the column types.

The legacy loop node (UI label “Table”) was merged into this node. loop is now a deprecated alias that auto-migrates to list on load — existing workflows keep working.

Configuration

Field	Type	Default	Description
Items / Columns	Dynamic table	One text column (“Items”)	Rows of values. Starts as a single text column; add columns by connecting producers to the bottom-left “+” handle
Column type	text / image-url / video-url / audio-url / json	text	Per-column data type. Determines the column’s handle type and the node’s auto-selected view mode

Each row is one iteration. The item/row counter shows the total number of entries.

Inputs & Outputs

Inputs:

• Bottom-left ”+” handle — connect a producer to add a new typed column
• Per-column input handles (col_<id>_in) — receive values into a specific column from upstream

Outputs:

• Single-column mode: each item is emitted in turn to downstream nodes
• Multi-column mode: per-column outputs (col_<id>) — each column’s value is available as a separate output per iteration

Best Practices

• Keep single-column list items consistent in format for predictable downstream behavior
• Use one concept per item — each item should be a complete, standalone prompt or value
• Add a column (via the “+” handle) only when you genuinely need a second variable per iteration — typed columns map cleanly to different input fields on downstream nodes
• Set each column’s type to match its content (image-url, video-url, etc.) so the view mode and downstream wiring resolve correctly

Common Use Cases

• Batch-generate images from a list of prompts (single column)
• Process multiple subjects through the same video generation pipeline
• Generate TTS audio for multiple text entries
• Generate character images with different names and descriptions per row (multi-column)
• Drive videos with varying prompts and reference-media URLs per row (multi-column)

Tips

• Press Enter to add a new row quickly
• Rows are processed in order from top to bottom
• Each row triggers a separate downstream execution — more rows means longer total runtime
• For very large batches, consider breaking into smaller lists to manage workflow size
• To close a fan-out (pick the best variant, count survivors, or merge results), feed the downstream output into a Reduce node