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nano-banana-edit

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🍌 Nano Banana Edit — Pro Pack on RunComfy

🍌 Nano Banana Edit — RunComfy专业套件

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.
bash
npx skills add agentspace-so/runcomfy-skills --skill nano-banana-edit -g
runcomfy.com · 编辑端点 · GitHub
Google Nano Banana 2 Edit 是Gemini系列闪存级图像模型的图生图编辑端点，托管在RunComfy Model API上。每次调用最多支持20张输入图片，可用于批量编辑和多参考变体生成。
bash
npx skills add agentspace-so/runcomfy-skills --skill nano-banana-edit -g

When to pick this model (vs siblings)

何时选择该模型（对比同类模型）

You wantUse
Preserve subject identity, swap background or clothingNano Banana Edit ✓
Edit up to 20 images consistently in one batchNano Banana Edit ✓
Localize edit to "X only" with spatial languageNano 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 scratchNano Banana 2 t2i (sibling skill)
If the user said "nano banana edit" / "edit with nano banana" explicitly, route here regardless.
你的需求使用模型
保留主体身份、替换背景或服装Nano Banana Edit ✓
一次批量处理最多20张图片，保持编辑一致性Nano Banana Edit ✓
使用空间语言定位编辑区域（如“仅修改X区域”）Nano Banana Edit ✓
编辑图片中的多语言文本（标识、标签等）GPT Image 2 edit
单参考图+精准局部编辑（如“让她手持X物品”）Flux Kontext
从零生成新图片Nano Banana 2 t2i（同类技能）
如果用户明确提到“nano banana edit” / “edit with nano banana”，无论需求如何都路由到该模型。

Prerequisites

前置条件

  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
    .
  1. RunComfy CLI — 
    npm i -g @runcomfy/cli
  2. RunComfy账号 — 执行
    runcomfy login
    会打开浏览器设备码登录流程。
  3. CI/容器环境 — 设置环境变量
    RUNCOMFY_TOKEN=<token>
    替代
    runcomfy login
    。

Endpoints + input schema

端点与输入schema

google/nano-banana-2/edit

google/nano-banana-2/edit

FieldTypeRequiredDefaultNotes
prompt
stringyes—Edit instruction. Lead with preservation, end with the change.
image_urls
arrayyes—1–20 publicly-fetchable HTTPS URLs.
number_of_images
intno11–4 outputs per call.
seed
intno—Reproducibility.
aspect_ratio
enumno
auto
auto
(follows input) or fixed ratios — lock for batch consistency.
resolution
enumno
1K
0.5K
/ 
1K
/ 
2K
/ 
4K
.
output_format
enumno
png
png
/ 
jpeg
/ 
webp
.
safety_tolerance
intno41 (strict) – 6 (permissive).
limit_generations
boolno—If true, restricts each round to one output.
enable_web_search
boolnofalseWeb grounding (extra cost / latency).
字段类型是否必填默认值说明
prompt
string是—编辑指令。先说明需保留的内容，最后说明修改内容。
image_urls
array是—1–20个可公开访问的HTTPS URL。
number_of_images
int否1每次调用生成1–4张输出图。
seed
int否—用于生成结果的可重复性。
aspect_ratio
enum否
auto
auto
（跟随输入图比例）或固定比例——批量编辑时锁定可保证一致性。
resolution
enum否
1K
0.5K
/ 
1K
/ 
2K
/ 
4K
。
output_format
enum否
png
png
/ 
jpeg
/ 
webp
。
safety_tolerance
int否41（严格）– 6（宽松）。
limit_generations
bool否—如果为true，每轮仅生成一张输出图。
enable_web_search
bool否false网络 grounding（额外成本/延迟）。

How to invoke

调用方式

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 <absolute/path>
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 <absolute/path>
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 <absolute/path>
单图背景替换，保留主体身份：
bash
runcomfy run google/nano-banana-2/edit \
  --input '{
    "prompt": "保留主体身份、姿势和服装不变。将背景转换为下雨的霓虹赛博朋克街道。",
    "image_urls": ["https://.../portrait.jpg"]
  }' \
  --output-dir <absolute/path>
锁定画幅的批量编辑：
bash
runcomfy run google/nano-banana-2/edit \
  --input '{
    "prompt": "将右下角的水印替换为干净的白色无衬线字体\"AURA\"。其余内容完全保持与输入图一致。",
    "image_urls": ["https://.../sku-1.jpg", "https://.../sku-2.jpg", "https://.../sku-3.jpg"],
    "aspect_ratio": "1:1",
    "resolution": "1K"
  }' \
  --output-dir <absolute/path>
目标性空间编辑（“仅修改左侧物体”）：
bash
runcomfy run google/nano-banana-2/edit \
  --input '{
    "prompt": "仅移除最左侧的物体。保留右侧两个物体、桌子和光线不变。",
    "image_urls": ["https://.../still-life.jpg"]
  }' \
  --output-dir <absolute/path>

Prompting — what actually works

提示词技巧——有效方法

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.
先说明保留内容，最后说明修改内容。始终以
"保留[身份/姿势/服装/品牌/画幅]不变。"
开头，然后用简洁的句子说明修改内容。模型会优先遵循开头的保留要求；末尾的保留要求容易被忽略。
使用空间语言定位编辑区域。“仅修改背景”、“左侧物体”、“右上角”、“标题上方”——具体的空间范围会被模型识别。“让它更X”这类模糊描述会导致结果偏离。
批量编辑一致性——编辑系列图片时，锁定
aspect_ratio
和
resolution
。整个批量使用相同的提示词格式，确保每张输出图风格统一，而非杂乱的变体。
分步小幅度修改。如果单次编辑结果偏离预期，拆分为两次：第一次仅修改背景，第二次替换主体服装。编辑效果更清晰，总成本相同（假设分辨率相近）。
多图变体生成——最多传入20张输入图，生成连贯的批量结果。适用于SKU图库、A/B测试、角色表变体等场景。
反模式：
  • 冗长的复合指令（“修改A、B、C和D”）——每增加一个修改范围，结果偏离的概率就会上升。
  • 使用被动语态的编辑指令（“背景应该被修改”）——使用祈使句。
  • 未说明保留目标——模型会细微修改面部/品牌。
  • 与输入图不匹配的宽高比——会导致裁剪或拉伸。

Where it shines

适用场景

Use caseWhy Nano Banana Edit
SKU gallery — same product on different backgroundsBatch of 20, identity-preserved, framing locked
Influencer / spokesperson background swapsStrong identity preservation across edits
Localized object removal / additionSpatial language honored
A/B variants for ad creativeSeed lock + multiple 
number_of_images
Brand-asset relocalizationSame composition with text / palette swap
使用场景选择Nano Banana Edit的原因
SKU图库——同一款产品搭配不同背景支持20张批量处理、保留主体身份、锁定画幅
网红/代言人背景替换多次编辑中仍能很好地保留主体身份
局部物体移除/添加能识别空间语言定位编辑区域
广告创意A/B变体支持种子锁定+多张
number_of_images
输出
品牌资产本地化调整保持构图不变，仅替换文本/配色

Sample prompts (verified to produce strong results)

示例提示词（已验证可生成优质结果）

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.
背景替换（页面示例）：
保留主体身份不变。将背景转换为下雨的
霓虹赛博朋克街道。
目标性文本替换：
完全保留瓶子、标签和光线不变。
仅将标签上的品牌文本从"ALPHA"替换为"AURA"，
保持相同字体粗细、居中对齐、黑底白字。
多图批量编辑一致性：
针对每张输入图：保留主体姿势和身份不变。
将背景转换为柔和的暖灰色工作室背景，带有轻微的
地面阴影。主体在画面中的位置比例与输入图一致。

Limitations

局限性

  • 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.
  • 每次调用支持1–20张输入图——第一张图作为主参考，其余图提供辅助线索。
  • 每次调用生成1–4张输出图。
  • 冗长的复合提示词会导致结果偏离——拆分为多次处理。
  • 启用网络搜索会增加延迟和成本——仅按需启用。
  • 编辑图片中的多语言文本，GPT Image 2 edit更合适。

Exit codes

退出码

codemeaning
0success
64bad CLI args
65bad input JSON / schema mismatch
69upstream 5xx
75retryable: timeout / 429
77not signed in or token rejected
Full reference: docs.runcomfy.com/cli/troubleshooting.
代码含义
0成功
64CLI参数错误
65输入JSON错误/schema不匹配
69上游服务5xx错误
75可重试：超时/429请求过多
77未登录或令牌被拒绝
完整参考：docs.runcomfy.com/cli/troubleshooting。

How it works

工作原理

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.
该技能调用
runcomfy run google/nano-banana-2/edit
并传入符合schema的JSON参数。CLI向
https://model-api.runcomfy.net/v1/models/google/nano-banana-2/edit
发送POST请求，轮询请求状态，获取结果，并将所有
.runcomfy.net
/
.runcomfy.com
链接的文件下载到
--output-dir
目录。按
Ctrl-C
会在退出前取消远程请求。

Security & Privacy

安全与隐私

  • 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.
  • 令牌存储：
    runcomfy login
    会将API令牌写入
    ~/.config/runcomfy/token.json
    ，权限为0600（仅所有者可读可写）。在CI/容器环境中，可设置环境变量
    RUNCOMFY_TOKEN
    完全绕过文件存储。
  • 输入边界：用户提示词通过
    --input
    以JSON字符串形式传递给CLI。CLI不会对提示词进行shell扩展，而是直接通过HTTPS将JSON主体传输到Model API。提示词内容不存在shell注入风险。
  • 第三方内容：你传入的图片/蒙版/视频URL由RunComfy模型服务器获取，而非本地CLI。请将外部URL视为不可信；基于图片的提示注入是所有图像/视频编辑模型的已知风险。
  • 出站端点：仅与
    model-api.runcomfy.net
    （请求提交）和
    *.runcomfy.net
    / 
    *.runcomfy.com
    （生成结果下载白名单）通信。无遥测、无回调。
  • 生成文件大小限制：CLI会终止任何超过2 GiB的单个下载，防止恶意或异常模型输出占满磁盘。