nano-banana-edit
Edit Images with Google Nano Banana 2 on RunComfy
Also available from: agentspace-so,runcomfy-com
Need to edit product photos, swap backgrounds, or batch process images? Nano Banana 2 Edit on RunComfy offers batch support for up to 20 images with strong subject identity preservation and spatial edit controls.
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Test it
Using "nano-banana-edit". User asks to swap background to a rainy neon cyberpunk street while keeping the subject identity
Expected outcome:
The model generates 1-4 images with the same subject preserved against a rainy neon cyberpunk background, with consistent pose and framing.
Using "nano-banana-edit". User asks to edit multiple product photos with a warm studio sweep background
Expected outcome:
Batch of 3-5 product images with consistent warm grey studio backgrounds and subject positioning, ready for e-commerce listing.
Security Audit
SafeStatic analysis flagged 78 potential issues, but evaluation reveals all are false positives. The skill is a thin wrapper for the RunComfy CLI that invokes `runcomfy run google/nano-banana-2/edit` to call a legitimate image editing API. The "external commands" detections are markdown bash command examples, not executable code. The "path traversal" flags reference placeholder parameter examples (`<absolute/path>`) in documentation. The "weak crypto" detection is frontmatter YAML. Network access is scoped to RunComfy API endpoints only. Security controls are properly documented including JSON string transmission (no shell injection) and secure token storage with mode 0600.
High Risk Issues (3)
Medium Risk Issues (1)
Low Risk Issues (2)
Risk Factors
🌐 Network access (3)
⚙️ External commands (5)
Quality Score
What You Can Build
E-commerce Product Photography
Batch edit product images with consistent backgrounds. Edit up to 20 SKU images in one call, preserving product identity while swapping studio backgrounds.
Marketing Campaign Creative
Create A/B variants for ad creative with seed-locked multiple outputs. Generate background swaps and localized edits for influencer and brand content.
Brand Asset Relocalization
Adjust brand assets for different markets while preserving composition. Consistent text and palette swaps across localized content variants.
Try These Prompts
Keep the subject identity unchanged. Convert the background into [description of desired background].
Remove the [specific object] only. Keep [list of elements to preserve], the [environment elements], and the lighting unchanged.
For each input image: keep the [subject identity/pose/clothing] unchanged. Convert the background to [description]. Center the subject at the same fraction of frame as the input.
For each input: preserve the [identity/brand element]. Replace [specific element] with [new description]. Maintain consistent lighting and color palette across all outputs.
Best Practices
- Lead with preservation instructions, end with the change. Always state what to keep first, then describe the edit.
- Use concrete spatial language for localized edits: 'background only', 'left object', 'upper-right corner'.
- Lock aspect_ratio and resolution for batch consistency. Use the same prompt grammar across batch edits.
Avoid
- Avoid long compound instructions (change A and B and C) which cause model drift.
- Do not use passive voice for edit instructions. Be imperative: 'Replace X with Y' not 'the background should be changed'.
- Do not skip preservation goals. State explicitly what to keep unchanged or the model will subtly modify faces and brands.