Seedream 5.0 Pro เปิดให้ใช้งานแล้ว | ลองใช้ในเครื่องสร้างรูปภาพ →
เข้าสู่ระบบ

Google Nano Banana Lite Edit API

google /

Google Nano Banana 2 Lite Edit transforms uploaded images with text instructions, supporting fast prompt-guided image editing, visual refinements, and creative changes with low latency. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
อินพุต

ว่าง

Transform this portrait into a cinematic post-apocalyptic survivor poster. Keep the woman's face, identity, pose, and jacket structure consistent. Add dust and subtle scratches on her face, change the background into a ruined city street with smoke, broken buildings, orange sunset light, and a red emergency flare glow from one side. Make her expression determined and emotional. Realistic cinematic lighting, gritty survival atmosphere, no gore, no face distortion.

$0.04ต่อครั้ง·~25 / $1

ต่อไป:

ตัวอย่างดูทั้งหมด

Transform this portrait into a cinematic post-apocalyptic survivor poster. Keep the woman's face, identity, pose, and jacket structure consistent. Add dust and subtle scratches on her face, change the background into a ruined city street with smoke, broken buildings, orange sunset light, and a red emergency flare glow from one side. Make her expression determined and emotional. Realistic cinematic lighting, gritty survival atmosphere, no gore, no face distortion.

Transform this portrait into a cinematic post-apocalyptic survivor poster. Keep the woman's face, identity, pose, and jacket structure consistent. Add dust and subtle scratches on her face, change the background into a ruined city street with smoke, broken buildings, orange sunset light, and a red emergency flare glow from one side. Make her expression determined and emotional. Realistic cinematic lighting, gritty survival atmosphere, no gore, no face distortion.

โมเดลที่เกี่ยวข้อง

README

Google Nano Banana 2 Lite Edit

Google Nano Banana 2 Lite Edit transforms uploaded images using natural-language instructions with a fast, lightweight image editing model. Upload one or more images, describe the edit you want, choose an output size and format, and receive the edited image URL in the standard WaveSpeed prediction response.

Why Choose This?

  • Instruction-based image editing
    Edit input images by describing the desired change in natural language.

  • Multiple image references
    Provide one or more images to guide the edit workflow.

  • Flexible aspect ratios
    Choose common square, portrait, landscape, and wide-format aspect ratio presets.

  • Simple editing controls
    The public form focuses on images, prompt, size, and output_format.

  • Single-image output
    Each request generates one edited image for predictable cost and behavior.

Parameters

ParameterRequiredDescription
imagesYesInput image URLs for editing.
promptYesEdit instruction describing how to transform the input image.
aspect_ratioNoOutput aspect ratio preset.
output_formatNoOutput image format: png or jpeg. Default: png.
enable_sync_modeNoWait for the result to be generated and uploaded before returning the response. API only. Synchronous requests may hit timeouts because Google inference time can fluctuate. Default: false.
enable_base64_outputNoReturn output as a BASE64 string instead of a URL. API only. Default: false.

How to Use

  1. Upload images — Provide one or more input images for editing.
  2. Write your edit prompt — Describe what should change and what should stay the same.
  3. Choose output size — Use auto or select a specific aspect ratio preset.
  4. Choose output format — Select png or jpeg depending on your workflow.
  5. Submit — Generate the edited image and retrieve the output URL.

Pricing

Output ImagesPrice
1$0.04

Best Use Cases

  • Photo editing — Change backgrounds, style, lighting, mood, or visual details.
  • Product visuals — Refine source assets for marketing, ecommerce, and product presentation.
  • Creative iteration — Try different edit prompts on the same source images.
  • Reference-based edits — Use multiple uploaded images as references for more guided edits.
  • Content production — Generate polished edited assets for social media, campaigns, thumbnails, and design workflows.

Pro Tips

  • Use clear edit instructions that describe both what should change and what should remain unchanged.
  • Upload sharp images with the main subject clearly visible.
  • Use multiple images when you need stronger reference guidance.
  • Use auto when you want the model to infer the best output ratio from the input and prompt.
  • Use png for higher-quality general output.
  • Use jpeg for smaller file sizes.
หมายเหตุ:เว็บไซต์นี้ใช้โมเดล AI ที่จัดหาโดยบุคคลที่สาม

Nano Banana 2 Lite Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/nano-banana-2-lite/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Nano Banana 2 Lite Edit below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "aspect_ratio": "1:1",
    "output_format": "png"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/google/nano-banana-2-lite/edit" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/google/nano-banana-2-lite/edit";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "aspect_ratio": "1:1",
        "output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "aspect_ratio": "1:1",
    "output_format": "png"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/google/nano-banana-2-lite/edit", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Nano Banana 2 Lite Edit API — Frequently asked questions

What is the Nano Banana 2 Lite Edit API?

Nano Banana 2 Lite Edit is a Google model for image editing, exposed as a REST API on WaveSpeedAI. Google Nano Banana 2 Lite Edit transforms uploaded images with text instructions, supporting fast prompt-guided image editing, visual refinements, and creative changes with low latency. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Nano Banana 2 Lite Edit API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/google/google-nano-banana-2-lite-edit.

How much does Nano Banana 2 Lite Edit cost per run?

Nano Banana 2 Lite Edit starts at $0.040 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Nano Banana 2 Lite Edit accept?

Key inputs: `prompt`, `images`, `aspect_ratio`, `enable_base64_output`, `enable_sync_mode`, `output_format`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/google/google-nano-banana-2-lite-edit.

How long does Nano Banana 2 Lite Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 50 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Nano Banana 2 Lite Edit outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Google). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Google Nano Banana Lite Edit API | WaveSpeedAI