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GPT Image 2 Edit

openai /

OpenAI's GPT Image 2 Edit enables image editing from natural-language instructions with one or more reference images. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
入力

待機中

A high-end fashion editorial portrait of a blonde Western female model wearing the earrings shown in the image.

The model is in her early 20s, with flawless, radiant skin and subtle freckles. She wears natural, dewy makeup with a soft blush and nude glossy lips. Her features are refined, with high cheekbones and a well-defined jawline. Her blonde hair is styled in a sleek low bun, neatly tucked behind her ears to fully showcase the earrings.

She is posed in an elegant side profile, with her eyes gently closed, conveying a calm, graceful, and serene expression.

The lighting is warm natural light, with soft botanical shadows cast across her face and shoulders, creating a soft yet high-contrast editorial atmosphere. The light enhances the texture of her skin as well as the reflective shine of the gold and pearl.

The background is minimal, in warm beige tones, softly blurred, presenting a premium lifestyle aesthetic.

Shot in an ultra-realistic style with fashion magazine quality, using an 85mm lens and shallow depth of field. The earrings are in sharp focus, with soft cinematic lighting, high detail, and a luxurious jewelry advertisement style, in 8K resolution.

$0.071回あたり·~14 / $1

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サンプルすべて表示

A high-end fashion editorial portrait of a blonde Western female model wearing the earrings shown in the image.

The model is in her early 20s, with flawless, radiant skin and subtle freckles. She wears natural, dewy makeup with a soft blush and nude glossy lips. Her features are refined, with high cheekbones and a well-defined jawline. Her blonde hair is styled in a sleek low bun, neatly tucked behind her ears to fully showcase the earrings.

She is posed in an elegant side profile, with her eyes gently closed, conveying a calm, graceful, and serene expression.

The lighting is warm natural light, with soft botanical shadows cast across her face and shoulders, creating a soft yet high-contrast editorial atmosphere. The light enhances the texture of her skin as well as the reflective shine of the gold and pearl.

The background is minimal, in warm beige tones, softly blurred, presenting a premium lifestyle aesthetic.

Shot in an ultra-realistic style with fashion magazine quality, using an 85mm lens and shallow depth of field. The earrings are in sharp focus, with soft cinematic lighting, high detail, and a luxurious jewelry advertisement style, in 8K resolution.

A high-end fashion editorial portrait of a blonde Western female model wearing the earrings shown in the image. The model is in her early 20s, with flawless, radiant skin and subtle freckles. She wears natural, dewy makeup with a soft blush and nude glossy lips. Her features are refined, with high cheekbones and a well-defined jawline. Her blonde hair is styled in a sleek low bun, neatly tucked behind her ears to fully showcase the earrings. She is posed in an elegant side profile, with her eyes gently closed, conveying a calm, graceful, and serene expression. The lighting is warm natural light, with soft botanical shadows cast across her face and shoulders, creating a soft yet high-contrast editorial atmosphere. The light enhances the texture of her skin as well as the reflective shine of the gold and pearl. The background is minimal, in warm beige tones, softly blurred, presenting a premium lifestyle aesthetic. Shot in an ultra-realistic style with fashion magazine quality, using an 85mm lens and shallow depth of field. The earrings are in sharp focus, with soft cinematic lighting, high detail, and a luxurious jewelry advertisement style, in 8K resolution.

関連モデル

README

OpenAI GPT Image 2 Edit

OpenAI GPT Image 2 Edit transforms one or more reference images using natural-language instructions. Upload your image, describe the changes you want, and the model generates a polished edited result with strong prompt alignment and production-ready quality.

Why Choose This?

  • Natural-language image editing Edit images by simply describing the changes you want in plain language — no manual masking or complex editing workflow required.

  • Works with reference images Use one or more input images as the visual source for edits, transformations, or style adjustments.

  • Flexible aspect ratios Generate edited outputs in square, portrait, or landscape formats for different publishing and design needs.

  • Production-ready API Access the model through a ready-to-use REST inference API for easy integration into apps, tools, and creative pipelines.

  • Fast and affordable Get high-quality image edits with simple usage-based pricing and no cold-start friction.

Parameters

ParameterRequiredDescription
imagesYesReference images to edit (up to 16)
promptYesText description of the desired edit
aspect_ratioNoAspect ratio: 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9. Auto-detected from input image if not specified.
resolutionNoOutput resolution: 1k (default), 2k, or 4k.
qualityNoImage quality: low, medium (default), or high.

How to Use

  1. Upload reference images — add up to 16 images you want to edit.
  2. Write your prompt — clearly describe the changes, style adjustments, or composition edits you want.
  3. Choose aspect ratio (optional) — use 1:1 for square, 2:3 or 9:16 for portrait, 3:2 or 16:9 for landscape, etc. Auto-detected from input image if not specified.
  4. Submit — run the model and download your edited image.

Example Prompt

Turn this product photo into a premium studio advertisement with soft cinematic lighting, a clean beige background, subtle shadows, realistic reflections, and luxury brand aesthetics

Pricing

Pricing varies by quality and resolution.

Prices below include one input image. Each additional input image adds $0.012.

Quality1k2k4k
low$0.02$0.03$0.04
medium$0.07$0.11$0.19
high$0.23$0.41$0.73

Best Use Cases

  • Product photo enhancement — Upgrade basic product shots into premium marketing visuals.
  • Creative retouching — Change backgrounds, lighting, styling, or composition with natural-language instructions.
  • Marketing adaptation — Rework existing brand assets into new campaign visuals without recreating them from scratch.
  • Social media content — Quickly edit images into platform-ready formats for posts, ads, and promos.
  • Design iteration — Explore multiple visual directions from the same base image with different prompts.
  • E-commerce optimization — Improve product presentation for listings, hero banners, and promotional creatives.

Pro Tips

  • Be specific about what should stay unchanged and what should be modified.
  • Mention visual style clearly, such as photorealistic, luxury editorial, minimal, cinematic, or flat lay.
  • Describe lighting, background, framing, and mood for more controllable results.
  • Use concise but precise prompts instead of overly vague instructions like “make it better.”
  • Try multiple aspect ratios when adapting the same edit for different placements.
  • If using multiple reference images, make sure they are visually clear and relevant to the intended output.

Notes

  • Both images and prompt are required fields.
  • Supported aspect ratios are 1:1, 3:2, 2:3, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, and 21:9. Auto-detected from input image if not specified.
  • This model is intended for image editing based on reference images and natural-language instructions.

Related Models

注記:本サイトは第三者が提供するAIモデルを使用しています。

Gpt Image 2 Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/openai/gpt-image-2/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 Gpt Image 2 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",
    "resolution": "1k",
    "quality": "medium",
    "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/openai/gpt-image-2/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/openai/gpt-image-2/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "aspect_ratio": "1:1",
        "resolution": "1k",
        "quality": "medium",
        "output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "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",
    "resolution": "1k",
    "quality": "medium",
    "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/openai/gpt-image-2/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)

Gpt Image 2 Edit API — Frequently asked questions

What is the Gpt Image 2 Edit API?

Gpt Image 2 Edit is a OpenAI model for image editing, exposed as a REST API on WaveSpeedAI. OpenAI's GPT Image 2 Edit enables image editing from natural-language instructions with one or more reference images. 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 Gpt Image 2 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/openai/openai-gpt-image-2-edit.

How much does Gpt Image 2 Edit cost per run?

Gpt Image 2 Edit starts at $0.070 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 Gpt Image 2 Edit accept?

Key inputs: `prompt`, `images`, `aspect_ratio`, `resolution`, `enable_base64_output`, `enable_sync_mode`. 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/openai/openai-gpt-image-2-edit.

How long does Gpt Image 2 Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 56 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 Gpt Image 2 Edit outputs commercially?

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

GPT Image 2 Edit | Fast Image Editing API | WaveSpeedAI