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google/gemini-2.5-flash-image-preview/edit

Google Gemini 2.5 Flash Image Preview is an image-to-image editing model with advanced creative controls for precise image edits. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
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Transform the entire video into a bold 1970s retro-futurist luxury world while preserving the man’s identity, facial features, walking motion, timing, and original camera movement.

Replace the modern hotel lobby with a glamorous space-age lounge featuring curved white architecture, glossy orange furniture, chrome surfaces, circular doorways, geometric carpets, and large panoramic windows overlooking a stylized futuristic city.

Change his dark suit into a cream-colored retro-futurist tailored suit with a burnt-orange shirt, wide collar, polished boots, and subtle metallic accessories. Transform the travel bag into a sleek rounded silver case with vintage space-age styling.

Keep his original actions unchanged: walking through the lobby, checking his wristwatch, stopping, and looking toward the elevators.

Use a bold orange, cream, teal, and chrome color palette, soft film grain, glossy reflections, symmetrical compositions, warm cinematic lighting, sophisticated 1970s science-fiction fashion editorial aesthetic.

$0.038per run·~26 / $1

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ExamplesView all

Transform the entire video into a bold 1970s retro-futurist luxury world while preserving the man’s identity, facial features, walking motion, timing, and original camera movement.

Replace the modern hotel lobby with a glamorous space-age lounge featuring curved white architecture, glossy orange furniture, chrome surfaces, circular doorways, geometric carpets, and large panoramic windows overlooking a stylized futuristic city.

Change his dark suit into a cream-colored retro-futurist tailored suit with a burnt-orange shirt, wide collar, polished boots, and subtle metallic accessories. Transform the travel bag into a sleek rounded silver case with vintage space-age styling.

Keep his original actions unchanged: walking through the lobby, checking his wristwatch, stopping, and looking toward the elevators.

Use a bold orange, cream, teal, and chrome color palette, soft film grain, glossy reflections, symmetrical compositions, warm cinematic lighting, sophisticated 1970s science-fiction fashion editorial aesthetic.

Transform the entire video into a bold 1970s retro-futurist luxury world while preserving the man’s identity, facial features, walking motion, timing, and original camera movement. Replace the modern hotel lobby with a glamorous space-age lounge featuring curved white architecture, glossy orange furniture, chrome surfaces, circular doorways, geometric carpets, and large panoramic windows overlooking a stylized futuristic city. Change his dark suit into a cream-colored retro-futurist tailored suit with a burnt-orange shirt, wide collar, polished boots, and subtle metallic accessories. Transform the travel bag into a sleek rounded silver case with vintage space-age styling. Keep his original actions unchanged: walking through the lobby, checking his wristwatch, stopping, and looking toward the elevators. Use a bold orange, cream, teal, and chrome color palette, soft film grain, glossy reflections, symmetrical compositions, warm cinematic lighting, sophisticated 1970s science-fiction fashion editorial aesthetic.

Related Models

README

Gemini 2.5 Flash Image

Gemini 2.5 Flash Image is Google’s state-of-the-art image generation and editing model. It is a new variant of the Gemini 2.5 family, specifically designed for fast, conversational, and multi-turn creative workflows. This model is made available to developers through the Gemini API, Google AI Studio, and Vertex AI.

Key Features

  • Native Image Generation and Editing: Gemini 2.5 Flash Image is a multimodal model that natively understands and generates images. This allows for a seamless, unified workflow for creating and editing visuals.
  • Multi-image Fusion: This powerful feature allows you to combine multiple input images into a single, cohesive, new visual. For example, you can integrate a product into a new scene or restyle a room by merging images of different furniture and decor.
  • Character and Style Consistency: A significant advancement is the ability to maintain a consistent character, object, or style across multiple prompts and images. This is essential for storytelling, branding, and generating a series of cohesive assets without needing time-consuming fine-tuning.
  • Conversational Editing: The model enables precise, targeted edits using natural language. You can make specific changes like blurring a background, removing an object, altering a subject’s pose, or colorizing a black-and-white photo by simply describing the desired outcome.
  • Visual Reasoning: Gemini 2.5 Flash Image benefits from the Gemini model’s deep world knowledge. It can go beyond simple photorealism to perform complex tasks that require genuine understanding, such as interpreting hand-drawn diagrams, assisting with educational queries, and following multi-step instructions.
  • SynthID Watermarking: To promote responsible AI and transparency, all images created or edited with Gemini 2.5 Flash Image are embedded with an invisible digital watermark from SynthID. This watermark helps identify the content as AI-generated or edited.
Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Gemini 2.5 Flash Image Preview Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/gemini-2.5-flash-image-preview/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 Gemini 2.5 Flash Image Preview Edit below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "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/gemini-2.5-flash-image-preview/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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"

# 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' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/google/gemini-2.5-flash-image-preview/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "aspect_ratio": "1:1",
        "output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
  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 = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "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/gemini-2.5-flash-image-preview/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 = 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", "deleted"}:
        raise RuntimeError(result)
    time.sleep(2)

Gemini 2.5 Flash Image Preview Edit API — Frequently asked questions

What is the Gemini 2.5 Flash Image Preview Edit API?

Gemini 2.5 Flash Image Preview Edit is a Google model for image editing, exposed as a REST API on WaveSpeedAI. Google Gemini 2.5 Flash Image Preview is an image-to-image editing model with advanced creative controls for precise image edits. 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 Gemini 2.5 Flash Image Preview 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/google/google-gemini-2.5-flash-image-preview-edit.

How much does Gemini 2.5 Flash Image Preview Edit cost per run?

Gemini 2.5 Flash Image Preview Edit starts at $0.038 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 Gemini 2.5 Flash Image Preview 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-gemini-2.5-flash-image-preview-edit.

How long does Gemini 2.5 Flash Image Preview Edit take to generate?

Reported generation time on WaveSpeedAI is around 31 seconds per request. This is an estimate, not a latency guarantee; queue time and input settings can change the total wait. live status is visible in the prediction record.

Can I use Gemini 2.5 Flash Image Preview Edit outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Google). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

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