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Nano Banana Edit

在RunComfy上用Google Nano Banana 2 (图像到图像编辑端点)来编辑图像. 纳诺·香蕉 编辑优点(保存主题身份,互换背景,用空间语言将编辑本地化,多图像批次编辑最多可达20个输入),计划,以及何时去GPT图像2编辑/Flux Kontext/Nano Banana 2 t2i取而代之. 通过当地的RunComfy CLI呼叫 " runcomfy run google/nano-banana-2/edit " 。 在"纳诺香蕉编辑","用纳米香蕉编辑","图像编辑纳米香蕉"上触发,或者任何明确要求用这个模型编辑的内容.

424014 安装量

官方网址:作者主页

技能介绍

先看中文介绍;官方 description 原文单独保留,不改写 SKILL.md。

做什么

在RunComfy上用Google Nano Banana 2 (图像到图像编辑端点)来编辑图像. 纳诺·香蕉 编辑优点(保存主题身份,互换背景,用空间语言将编辑本地化,多图像批次编辑最多可达20个输入),计划,以及何时去GPT图像2编辑/Flux Kontext/Nano Banana 2 t2i取而代之. 通过当地的RunComfy CLI呼叫 " runcomfy run google/nano-banana-2/edit " 。 在"纳诺香蕉编辑","用纳米香蕉编辑","图像编辑纳米香蕉"上触发,或者任何明确要求用这个模型编辑的内容.

何时用

官方 description 未单独写出 Use when。按规范,代理会在用户任务与这段 description 的关键词匹配时激活本技能。

代理如何加载

按 Agent Skills 渐进披露:启动时只加载 name 与 description(约 100 token);任务匹配后才读入整份 SKILL.md 正文;scripts/、references/、assets/ 仅在需要时再读。 本文件正文结构:Nano Banana Edit — Pro Pack on RunComfy、When to pick this model (vs siblings)、Prerequisites、Endpoints + input schema、`google/nano-banana-2/edit`、How to invoke。 其中含规范建议的小节:边界情况。

文件分析

文件分析:这是一份仅含 SKILL.md 的指令型技能,代理激活后整份正文进入上下文。

官方 description(原文)

Edit images with Google Nano Banana 2 (image-to-image edit endpoint) on RunComfy. Documents Nano Banana Edit's strengths (preserve subject identity, swap background, localize edits with spatial language, multi-image batch edits up to 20 inputs), the schema, and when to route to GPT Image 2 edit / Flux Kontext / Nano Banana 2 t2i instead. Calls `runcomfy run google/nano-banana-2/edit` through the local RunComfy CLI. Triggers on "nano banana edit", "edit with nano banana", "image edit nano banana", or any explicit ask to edit with this model.

Nano Banana Edit — Pro Pack on RunComfyWhen to pick this model (vs siblings)PrerequisitesEndpoints + input schema`google/nano-banana-2/edit`How to invokePrompting — what actually worksWhere it shinesSample prompts (verified to produce strong results)LimitationsExit codesHow it works

· 许可:MIT

来源分类:skills.sh agent-skill

SKILL.md 与 Agent 调用

官方规范 ↗
name
nano-banana-edit
description
Edit images with Google Nano Banana 2 (image-to-image edit endpoint) on RunComfy. Documents Nano Banana Edit's strengths (preserve subject identity, swap background, localize edits with spatial language, multi-image batch edits up to 20 inputs), the schema, and when to route to GPT Image 2 edit / Flux Kontext / Nano Banana 2 t2i instead. Calls `runcomfy run google/nano-banana-2/edit` through the local RunComfy CLI. Triggers on "nano banana edit", "edit with nano banana", "image edit nano banana", or any explicit ask to edit with this model.
许可
MIT
  1. 发现技能客户端向 Agent 提供名称与描述目录。
  2. 匹配与调用用户指定或任务匹配后,载入 SKILL.md 指令。
  3. 按需加载按步骤读取参考文档、使用脚本与素材。

具体调用语法与可用工具以目标 Agent 客户端为准。 查看调用机制说明 ↗

安装这个技能

Skills CLI ↗

先选择目标 Agent 和安装范围,保留技能包的附属文件,安装后检查客户端能否发现该技能。

交给 Agent 安装

复制安装指令给支持 Agent Skills 的代理,确认其中的目标目录与客户端匹配。

把 Agent Skill「nano-banana-edit」安装到我的项目:SKILL.md 原文与官方 description 见 https://zicq.com/zh/skills/skl-b360346dd4ba71c5-Nano-Banana-Edit.html
请存为 .cursor/skills/nano-banana-edit/SKILL.md 或 .claude/skills/nano-banana-edit/SKILL.md,frontmatter 的 name 与 description 保持原样,不要改写。

GitHub 完整包 ↗

终端安装 · Skills CLI

需要 Node.js 与 npx。先查看仓库技能列表,确认实际名称。

npx skills add 'https://github.com/prime-skills/runcomfy-agent-skills' --list

npx skills add 'https://github.com/prime-skills/runcomfy-agent-skills' --skill 'nano-banana-edit'

CLI 会交互选择目标 Agent,默认安装到项目;用户级安装使用 -g。先通过查看命令核对仓库内容,再用 npx skills list 检查已安装技能。

阅读排版
--- name: nano-banana-edit displayName: "Nano Banana Edit — Pro Pack on RunComfy" description: > Edit images with Google Nano Banana 2 (image-to-image edit endpoint) on RunComfy. Documents Nano Banana Edit's strengths (preserve subject identity, swap background, localize edits with spatial language, multi-image batch edits up to 20 inputs), the schema, and when to route to GPT Image 2 edit / Flux Kontext / Nano Banana 2 t2i instead. Calls `runcomfy run google/nano-banana-2/edit` through the local RunComfy CLI. Triggers on "nano banana edit", "edit with nano banana", "image edit nano banana", or any explicit ask to edit with this model. homepage: https://www.runcomfy.com license: MIT --- # Nano Banana Edit — Pro Pack on RunComfy [runcomfy.com](https://www.runcomfy.com/?utm_source=skills.sh&utm_medium=skill&utm_campaign=nano-banana-edit) · [Edit endpoint](https://www.runcomfy.com/models/google/nano-banana-2/edit?utm_source=skills.sh&utm_medium=skill&utm_campaign=nano-banana-edit) · [GitHub](https://github.com/agentspace-so/runcomfy-skills/tree/main/nano-banana-edit) 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. ```bash npx skills add agentspace-so/runcomfy-skills --skill nano-banana-edit -g ``` ## When to pick this model (vs siblings) | You want | Use | |---|---| | Preserve subject identity, swap background or clothing | **Nano Banana Edit** | | Edit up to 20 images consistently in one batch | **Nano Banana Edit** | | Localize edit to "X only" with spatial language | **Nano Banana Edit** | | Edit multilingual text inside the image (signs, labels) | GPT Image 2 edit | | Single ref + precise local edit ("she's now holding X") | Flux Kontext | | Generate a new image from scratch | Nano Banana 2 t2i (sibling skill) | 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=` instead of `runcomfy login`. ## Endpoints + input schema ### `google/nano-banana-2/edit` | Field | Type | Required | Default | Notes | |---|---|---|---|---| | `prompt` | string | yes | — | Edit instruction. Lead with preservation, end with the change. | | `image_urls` | array | yes | — | **1–20** publicly-fetchable HTTPS URLs. | | `number_of_images` | int | no | 1 | 1–4 outputs per call. | | `seed` | int | no | — | Reproducibility. | | `aspect_ratio` | enum | no | `auto` | `auto` (follows input) or fixed ratios — lock for batch consistency. | | `resolution` | enum | no | `1K` | `0.5K` / `1K` / `2K` / `4K`. | | `output_format` | enum | no | `png` | `png` / `jpeg` / `webp`. | | `safety_tolerance` | int | no | 4 | 1 (strict) – 6 (permissive). | | `limit_generations` | bool | no | — | If true, restricts each round to one output. | | `enable_web_search` | bool | no | false | Web grounding (extra cost / latency). | ## How to invoke **Single-image background swap, identity preserved:** ```bash 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 ``` **Batch edit with locked framing:** ```bash 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 ``` **Targeted spatial edit ("left object only"):** ```bash 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 ``` ## 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 | Use case | Why Nano Banana Edit | |---|---| | **SKU gallery — same product on different backgrounds** | Batch of 20, identity-preserved, framing locked | | **Influencer / spokesperson background swaps** | Strong identity preservation across edits | | **Localized object removal / addition** | Spatial language honored | | **A/B variants for ad creative** | Seed lock + multiple `number_of_images` | | **Brand-asset relocalization** | Same composition with text / palette swap | ## Sample prompts (verified to produce strong results) **Background swap (page example):** ``` Keep the subject identity unchanged. Convert the background into a rainy neon 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 subtle floor shadow. Center the subject at the same fraction of frame as the input. ``` ## 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 | code | meaning | |---|---| | 0 | success | | 64 | bad CLI args | | 65 | bad input JSON / schema mismatch | | 69 | upstream 5xx | | 75 | retryable: timeout / 429 | | 77 | not signed in or token rejected | Full reference: [docs.runcomfy.com/cli/troubleshooting](https://docs.runcomfy.com/cli/troubleshooting?utm_source=skills.sh&utm_medium=skill&utm_campaign=nano-banana-edit). ## 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.

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