Nano Banana Edit — Pro Pack on RunComfy

Nano Banana Edit — Pro Pack on RunComfy

runcomfy.com · Edit endpoint · GitHub

Google Nano Banana 2 Edit — the image-to-image edit endpoint of the Gemini-family flash-tier image model — hosted on the RunComfy Model API. Up to 20 input images per call for batch edits and multi-reference variation.

npx skills add agentspace-so/runcomfy-skills --skill nano-banana-edit -g

When to pick this model (vs siblings)

If the user said "nano banana edit" / "edit with nano banana" explicitly, route here regardless.

Prerequisites

1. RunComfy CLI — npm i -g @runcomfy/cli
2. RunComfy account — runcomfy login opens a browser device-code flow.
3. CI / containers — set RUNCOMFY_TOKEN=<token> instead of runcomfy login.

Endpoints + input schema

google/nano-banana-2/edit

How to invoke

Single-image background swap, identity preserved:

runcomfy run google/nano-banana-2/edit \  --input '{    "prompt": "Keep the subject identity, pose, and clothing unchanged. Convert the background into a rainy neon cyberpunk street.",    "image_urls": ["https://.../portrait.jpg"]  }' \  --output-dir <absolute/path>

Batch edit with locked framing:

runcomfy run google/nano-banana-2/edit \  --input '{    "prompt": "Replace the watermark in the bottom-right with the text \"AURA\" in clean white sans-serif. Keep everything else exactly as in the input.",    "image_urls": ["https://.../sku-1.jpg", "https://.../sku-2.jpg", "https://.../sku-3.jpg"],    "aspect_ratio": "1:1",    "resolution": "1K"  }' \  --output-dir <absolute/path>

Targeted spatial edit ("left object only"):

runcomfy run google/nano-banana-2/edit \  --input '{    "prompt": "Remove the leftmost object only. Keep the right two objects, the table, and the lighting unchanged.",    "image_urls": ["https://.../still-life.jpg"]  }' \  --output-dir <absolute/path>

Prompting — what actually works

Preservation first, change last. Always lead with "Keep [identity / pose / clothing / brand / framing] unchanged." Then state the change in one clean sentence. Models honor what's stated up front; tail-end preservations get ignored.

Localize with spatial language. "background only", "the left object", "the upper-right corner", "above the headline" — concrete spatial scopes are honored. "make it more X" is vague and drifts.

Batch consistency — when editing a series, lock aspect_ratio and resolution. Use the same prompt grammar across the batch so each output reads as a sibling, not a remix.

Iterate small. If a one-pass edit drifts, split into two: pass 1 changes background only, pass 2 swaps the subject's outfit. Cleaner edits, same total cost (assuming similar resolution).

Multi-image variation — pass up to 20 inputs to get a coherent batch. Useful for SKU galleries, A/B testing, character sheet variations.

Anti-patterns:

• Long compound instructions ("change A and B and C and D") — drift increases per added scope.
• Edit instructions written in passive voice ("the background should be changed") — be imperative.
• Missing preservation goals — model will subtly rewrite the face / brand.
• Aspect ratios that don't match input — causes crops or stretches.

Where it shines

Sample prompts (verified to produce strong results)

Background swap (page example):

Keep the subject identity unchanged. Convert the background into a rainyneon cyberpunk street.

Targeted text replacement:

Keep the bottle, label, and lighting exactly as in the input.Replace only the brand text on the label from "ALPHA" to "AURA",same font weight, centered, white on black.

Multi-image batch consistency:

For each input image: keep the subject's pose and identity unchanged.Convert the background to a soft warm-grey studio sweep with subtlefloor shadow. Center the subject at the same fraction of frame as theinput.

Limitations

• 1–20 input images per call — the first is treated as primary; the rest provide auxiliary cues.
• 1–4 outputs per call.
• Long compound prompts drift — split into multiple passes.
• Web search adds latency + cost — only enable on demand.
• For multilingual in-image text edits, GPT Image 2 edit wins.

Exit codes

Full reference: docs.runcomfy.com/cli/troubleshooting.

How it works

The skill invokes runcomfy run google/nano-banana-2/edit with a JSON body matching the schema. The CLI POSTs to https://model-api.runcomfy.net/v1/models/google/nano-banana-2/edit, polls the request, fetches the result, and downloads any .runcomfy.net/.runcomfy.com URL into --output-dir. Ctrl-C cancels the remote request before exit.

Security & Privacy

• Token storage: runcomfy login writes the API token to ~/.config/runcomfy/token.json with mode 0600 (owner-only read/write). Set RUNCOMFY_TOKEN env var to bypass the file entirely in CI / containers.
• Input boundary: the user prompt is passed as a JSON string to the CLI via --input. The CLI does NOT shell-expand the prompt; it transmits the JSON body directly to the Model API over HTTPS. No shell injection surface from prompt content.
• Third-party content: image / mask / video URLs you pass are fetched by the RunComfy model server, not by the CLI on your machine. Treat external URLs as untrusted; image-based prompt injection is a known risk for any image-edit / video-edit model.
• Outbound endpoints: only model-api.runcomfy.net (request submission) and *.runcomfy.net / *.runcomfy.com (download whitelist for generated outputs). No telemetry, no callbacks.
• Generated-file size cap: the CLI aborts any single download > 2 GiB to prevent disk-fill from a malicious or runaway model output.