Collections

ComfyUI and generative AI

If you generate thousands of images or video clips per project in ComfyUI, the Zegami Batch Export node sends every output — plus its full workflow graph — straight into a Zegami collection, where you can filter by parameter, sort by similarity, and publish the best as a shareable gallery.

Last updated 2026-06-03

Why connect ComfyUI to Zegami

Generative-AI workflows produce batches, not single images — thousands of variations across seeds, samplers, CFG values, checkpoints, and prompts. Folder-scrolling doesn’t scale. With Zegami you get a visual grid of every output, filter and sort by any generation parameter, cluster by visual similarity (UMAP) to spot duplicates and outliers, and share a public link as the deliverable.

The integration has two halves:

  1. The Zegami Batch Export ComfyUI node (open-source, MIT) that captures and uploads each generation.
  2. The Zegami side: a collection-scoped API key, and the Calculated Columns → From JSON path tool that turns the captured workflow graph into filterable columns.

1. Create a target collection and a scoped key

  1. Create a collection in Zegami (empty is fine — the node will fill it).
  2. Open the collection’s Settings → API access tab.
  3. Click Generate ComfyUI key. The key is scoped to this collection only — it can’t touch the rest of your account, so it’s safe to keep in a shared workflow file. Copy it now; it’s shown only once.

A scoped key is the recommended choice for ComfyUI. (You can also mint account-wide keys under Account → API keys, but those grant access to every collection you can reach.)

2. Install and configure the node

Install ComfyUI-Zegami from ComfyUI Manager (search “Zegami”), or clone github.com/zegami/comfyui-zegami into ComfyUI/custom_nodes. Video export also needs ffmpeg on your PATH.

Provide your key in priority order (the node never reads it from the workflow JSON, so shared workflows stay credential-free):

  1. ZEGAMI_API_KEY environment variable (recommended)
  2. ~/.zegami/config.json: { "api_key": "zeg_…" }
  3. the node’s api_key_override input

3. Wire up and queue

Drop the Zegami Batch Export node inline anywhere — it passes images through, so it can sit before a Save node.

  • Connect your decoded images to the images input (the primary path: each item in the batch becomes a separate collection item).
  • For generative video (Wan / Hunyuan / LTX / Mochi), connect frames to the video input; the node encodes an MP4 and a poster thumbnail.
  • Set collection_id to your collection’s id.
  • Optionally set tags (e.g. a campaign or client name) and notes.

Queue the prompt. Switch to Zegami and your generations appear in the grid as the pipeline processes them. The node uploads on a background queue and never slows or breaks a generation — if Zegami is unreachable, your images are still saved locally.

4. Turn the workflow graph into columns

Every output carries its full ComfyUI prompt graph as an opaque _comfy_json field. To make parameters filterable:

  1. Open the collection, go to the views panel and Add calculated column.
  2. Switch to the From JSON path mode.
  3. Use the Suggested columns chips — Zegami auto-detects common nodes (KSampler, LoraLoader, CheckpointLoaderSimple, CLIPTextEncode) and offers one-click columns for seed, steps, cfg, sampler, scheduler, model, lora, and the positive/negative prompts.
  4. Or extract any field by hand: pick the _comfy_json source column, enter a JSON path (e.g. $.prompt.6.inputs.seed), choose a type, and preview.

The columns persist with the collection, so they re-apply every time it loads. Now you can filter by sampler, sort by CFG, group by checkpoint, or build a scatter plot of seed vs. any metric.

5. Triage, compare, and publish

  • Filter by any extracted parameter to narrow thousands of outputs to the handful worth keeping (Filtering & search).
  • Similarity / UMAP surfaces near-duplicates and structural clusters — invaluable for auditing a LoRA or checkpoint training set.
  • Video items show a play badge and duration in the grid; hover to preview (where supported), and open the inspector for full playback.
  • Publish the curated view as a public, shareable collection (Sharing & publishing) — the gallery itself becomes the client deliverable.

Plan limits

There’s no hard per-collection item cap — your plan’s storage is the real ceiling (shown on Organisation → Plan, with a rough “≈ N images / N video clips” estimate). A studio pushing 10k+ video items should keep an eye on it; Team plans add metered storage beyond the included quota.