Fan-Out and Parallel Runs

Fan-out turns one edge into many runs: when the data flowing across it is an array, the downstream nodes execute once per item. Collect does the reverse — it gathers a fanned-out region's per-item results back into a single array. Together they're visual map/reduce.

Edge modes

Click an edge to set its mode:

• None (default) — data passes through unchanged.
• Fan-out — the array on this edge is unpacked; everything downstream runs once per item. Reference the current item in node fields with {{ item }}, or {{ item.field }} for arrays of objects.
• Collect — closes a fan-out region: per-item results are gathered into one array, and downstream nodes run once.

Fan-out and collect are mutually exclusive on a single edge — fan out on the way in, collect on the way out.

Fanning out from an LLM

A powerful pattern: have an LLM generate the list. When a fan-out edge originates from an LLM node, PixelFlow automatically adds format instructions to the model so it outputs a clean JSON array matching the {{ item.* }} fields your downstream nodes use — visible on the node as a read-only Instructions field.

Input (topic)
  → LLM "write 5 image prompts about {{ input_1 }}"
  → fan-out → image model (5 parallel runs)
  → collect → Output (gallery of 5)

Comparing models in parallel

Fan-out isn't the only way to parallelize — a single output can also feed multiple different nodes directly. Wire one prompt input into three different image models side by side and run once to compare them on identical input.

Use cases

• Batch processing — caption every image in a list, upscale a folder's worth of assets
• Prompt sweeps — generate N variants and pick the best
• Model comparison — same input across competing models
• Pipeline scaling — publish the workflow as an API and the fan-out scales with the array your callers send