nano-banana-2-gemini（nano banana 2 Gemini 搜索）

nano-banana-2

Gemini image generation and editing via gemini-3.1-flash-image-preview. All output images are written to .nano-banana/ in the current project directory.

Prerequisites

GEMINI_API_KEY must be set in the environment. Verify with:

echo $GEMINI_API_KEY

If empty, see rules/setup.md. For output handling and security guidelines, see rules/security.md.

Workflow

Follow this escalation pattern:

1. Generate - Create a new image from a text prompt only.
2. Edit - Modify an existing local image with a text instruction.
3. Search-Grounded - Generate informed by live web/image search results (use when current visual references, styles, or real-world accuracy matter).

Output & Organization

All images are saved to .nano-banana/ in the current working directory. Add .nano-banana/ to .gitignore to prevent generated assets from being committed.

mkdir -p .nano-banana
echo ".nano-banana/" >> .gitignore

Naming conventions:

.nano-banana/gen-{slug}-{timestamp}.png
.nano-banana/edit-{slug}-{timestamp}.png
.nano-banana/search-{slug}-{timestamp}.png

Where {slug} is a short kebab-case label from the first 4-5 words of the prompt, and {timestamp} is YYYYMMDD-HHMMSS.

After saving, open to confirm the result:

open "$(ls -t .nano-banana/*.png | head -1)"

API Reference

Resolution options (imageConfig.imageSize)

Aspect ratio options (imageConfig.aspectRatio)

1:1, 16:9, 9:16, 1:4, 4:1, 1:8, 8:1, 2:3, 3:2

Thinking mode (generationConfig.thinkingConfig.thinkingBudget)

An integer token budget. Set inside generationConfig, not at the top level.

Operations

1. Generate (text-to-image)

python3 - <<'PYEOF'
import os, base64, json, urllib.request, datetime

api_key = os.environ["GEMINI_API_KEY"]
prompt  = "a majestic mountain at sunrise, photorealistic"
slug    = "mountain-sunrise"
size    = "1024"    # 512 | 1024 | 2048 | 4096
aspect  = "16:9"   # 1:1 | 16:9 | 9:16 | 1:4 | 4:1 | 1:8 | 8:1 | 2:3 | 3:2
thinking = 0       # 0 = off, 1024 = light, 8192 = deep

payload = {
    "contents": [{"parts": [{"text": prompt}]}],
    "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "imageConfig": {"imageSize": size, "aspectRatio": aspect},
        "thinkingConfig": {"thinkingBudget": thinking}
    }
}

url = (
    "https://generativelanguage.googleapis.com/v1beta/models/"
    "gemini-3.1-flash-image-preview:generateContent"
)
req = urllib.request.Request(
    url,
    data=json.dumps(payload).encode(),
    headers={"Content-Type": "application/json", "x-goog-api-key": api_key},
    method="POST"
)
with urllib.request.urlopen(req) as resp:
    data = json.load(resp)

ts  = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
out = f".nano-banana/gen-{slug}-{ts}.png"
os.makedirs(".nano-banana", exist_ok=True)

for part in data["candidates"][0]["content"]["parts"]:
    if part.get("inlineData", {}).get("mimeType", "").startswith("image/"):
        with open(out, "wb") as f:
            f.write(base64.b64decode(part["inlineData"]["data"]))
        print(f"Saved: {out}")
        break
    elif part.get("text"):
        print("Model:", part["text"])
PYEOF

2. Edit (image-to-image)

The source image is base64-encoded and sent alongside the instruction text. Supports PNG and JPEG inputs.

python3 - <<'PYEOF'
import os, base64, json, urllib.request, datetime

api_key     = os.environ["GEMINI_API_KEY"]
source_img  = "path/to/source.png"          # change to actual path
instruction = "Make the sky purple and add stars"
slug        = "purple-sky-stars"
size        = "1024"
aspect      = "1:1"
thinking    = 0   # 0 = off, 1024 = light, 8192 = deep

with open(source_img, "rb") as f:
    img_b64 = base64.b64encode(f.read()).decode()

ext  = source_img.rsplit(".", 1)[-1].lower()
mime = "image/jpeg" if ext in ("jpg", "jpeg") else "image/png"

payload = {
    "contents": [{
        "parts": [
            {"text": instruction},
            {"inline_data": {"mime_type": mime, "data": img_b64}}
        ]
    }],
    "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "imageConfig": {"imageSize": size, "aspectRatio": aspect},
        "thinkingConfig": {"thinkingBudget": thinking}
    }
}

url = (
    "https://generativelanguage.googleapis.com/v1beta/models/"
    "gemini-3.1-flash-image-preview:generateContent"
)
req = urllib.request.Request(
    url,
    data=json.dumps(payload).encode(),
    headers={"Content-Type": "application/json", "x-goog-api-key": api_key},
    method="POST"
)
with urllib.request.urlopen(req) as resp:
    data = json.load(resp)

ts  = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
out = f".nano-banana/edit-{slug}-{ts}.png"
os.makedirs(".nano-banana", exist_ok=True)

for part in data["candidates"][0]["content"]["parts"]:
    if part.get("inlineData", {}).get("mimeType", "").startswith("image/"):
        with open(out, "wb") as f:
            f.write(base64.b64decode(part["inlineData"]["data"]))
        print(f"Saved: {out}")
        break
    elif part.get("text"):
        print("Model:", part["text"])
PYEOF

3. Search-Grounded Generation

Adds googleSearch with both webSearch and imageSearch types to ground the output in live web data. Use when the prompt references real-world subjects, current styles, recent events, or factual visual accuracy.

python3 - <<'PYEOF'
import os, base64, json, urllib.request, datetime

api_key  = os.environ["GEMINI_API_KEY"]
prompt   = "Generate a product photo of the latest iPhone model"
slug     = "latest-iphone-product"
size     = "1024"
aspect   = "1:1"
thinking = 8192   # deeper thinking recommended for grounded generation

payload = {
    "contents": [{"parts": [{"text": prompt}]}],
    "tools": [{
        "googleSearch": {
            "searchTypes": ["webSearch", "imageSearch"]
        }
    }],
    "generationConfig": {
        "responseModalities": ["TEXT", "IMAGE"],
        "imageConfig": {"imageSize": size, "aspectRatio": aspect},
        "thinkingConfig": {"thinkingBudget": thinking}
    }
}

url = (
    "https://generativelanguage.googleapis.com/v1beta/models/"
    "gemini-3.1-flash-image-preview:generateContent"
)
req = urllib.request.Request(
    url,
    data=json.dumps(payload).encode(),
    headers={"Content-Type": "application/json", "x-goog-api-key": api_key},
    method="POST"
)
with urllib.request.urlopen(req) as resp:
    data = json.load(resp)

ts  = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
out = f".nano-banana/search-{slug}-{ts}.png"
os.makedirs(".nano-banana", exist_ok=True)

for part in data["candidates"][0]["content"]["parts"]:
    if part.get("inlineData", {}).get("mimeType", "").startswith("image/"):
        with open(out, "wb") as f:
            f.write(base64.b64decode(part["inlineData"]["data"]))
        print(f"Saved: {out}")
        break
    elif part.get("text"):
        print("Model:", part["text"])
PYEOF

Working with Results

# List all generated images
ls -lh .nano-banana/

# Open the most recent
open "$(ls -t .nano-banana/*.png | head -1)"

# Open all images generated today
open .nano-banana/*$(date +%Y%m%d)*.png

Error Handling

If the API returns an error, the response will contain an error key. Print it with:

python3 -c "
import json, sys
d = json.loads(sys.stdin.read())
if 'error' in d:
    print('API Error:', json.dumps(d['error'], indent=2))
elif not d.get('candidates'):
    print('No candidates:', json.dumps(d, indent=2))
"

Common errors: