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GPT Image 1.5 Edit is OpenAI’s image model for precise, natural-language edits. Add/remove objects, swap backgrounds, retouch faces, adjust colors/lighting, edit text/graphics, crop/resize, and apply hex color control. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
Input

Siap

Change the background to the snowy night.

$0.1per run·~10 / $1

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ContohLihat semua

Change the background to the snowy night.

Change the background to the snowy night.

Make the desktop tidy

Make the desktop tidy

Transform this painting into a Japanese manga style

Transform this painting into a Japanese manga style

Replace the phone in the picture with headphones.

Replace the phone in the picture with headphones.

Have the people in picture one and picture two take a photo together.

Have the people in picture one and picture two take a photo together.

Model Terkait

README

OpenAI GPT Image 1.5 Edit

GPT Image 1.5 Edit is a cost-efficient image editing model powered by OpenAI’s GPT image technology. It enables users to refine, modify, or transform existing images using natural language instructions, while maintaining the original style, composition, and visual integrity.

🌟 Key Features

  • 🧠 Strong Visual Understanding Understands complex textual instructions and applies targeted edits that match intent and context.

  • 🎨 Intelligent Image Editing Add, remove, or modify elements in an image with precision — from subtle adjustments to full stylistic transformations.

  • 🖼 Multi-Image Support Accepts one or more image inputs to guide the edit or style reference process.

  • 💡 Context-Aware Refinement Preserves the key artistic or photographic features (lighting, tone, pose) while applying changes only where needed.

  • 💰 Efficient and Accessible Professional-quality visual editing at low cost, ideal for rapid prototyping, design iteration, or creative workflows.

⚙️ Parameters

ParameterDescription
prompt*Describe how you want to edit or modify the image (e.g., “change outfit colors to pastel tones, add neon city lights in the background”).
images*Upload one or more reference images (JPG / PNG) to be edited or used as visual input.
qualityOutput quality tier: low / medium / high.
input_fidelityWhich allows you to better preserve details from the input images in the output. This is especially useful when using images that contain elements like faces or logos that require accurate preservation in the generated image.
sizeOutput size: auto (default), 1024×1024, 1024×1536, or 1536×1024.

💡 Example Prompt

Three fashionable young women in a nighttime urban scene, showcasing Y2K and streetwear aesthetics. Each has distinct styling: plaid shirt with ripped jeans, off-shoulder top with retro socks and chunky sneakers, crop top with cowboy boots and accessories. Enhance lighting and color balance for a cinematic look.

💰 Pricing

Reference table (total_price per image edit):

  • Prices include one input image processed at low input fidelity.
  • Each additional input image adds $0.01.
  • input_fidelity: high (the default) adds $0.05 per input image on top.
Qualityauto1024×10241024×15361536×1024
low$0.03$0.02$0.03$0.03
medium$0.07$0.05$0.07$0.07
high$0.21$0.15$0.21$0.21

Use Cases

  • Product & Fashion Editing — Adjust outfits, lighting, or background for catalog or campaign visuals.
  • UI/UX & Brand Design — Apply aesthetic refinements to mockups or visual assets.
  • Creative Direction — Evolve photo concepts while preserving original mood and framing.
  • Photography & Illustration — Fix, enhance, or restyle images using natural text prompts.
Catatan:Situs web ini menggunakan model AI yang disediakan oleh pihak ketiga. Harga dokumentasi hanya sebagai referensi dan dapat kedaluwarsa. Tombol Generate menampilkan perkiraan; tagihan akhir tugas yang berlaku.

Gpt Image 1.5 Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/openai/gpt-image-1.5/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 1.5 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",
    "size": "auto",
    "background": "opaque",
    "quality": "medium",
    "input_fidelity": "high",
    "output_format": "jpeg"
}
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-1.5/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-1.5/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "size": "auto",
        "background": "opaque",
        "quality": "medium",
        "input_fidelity": "high",
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "size": "auto",
    "background": "opaque",
    "quality": "medium",
    "input_fidelity": "high",
    "output_format": "jpeg"
}

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-1.5/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 1.5 Edit API — Frequently asked questions

What is the Gpt Image 1.5 Edit API?

Gpt Image 1.5 Edit is a OpenAI model for image editing, exposed as a REST API on WaveSpeedAI. GPT Image 1.5 Edit is OpenAI’s image model for precise, natural-language edits. Add/remove objects, swap backgrounds, retouch faces, adjust colors/lighting, edit text/graphics, crop/resize, and apply hex color control. 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 1.5 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-1.5-edit.

How much does Gpt Image 1.5 Edit cost per run?

Gpt Image 1.5 Edit starts at $0.10 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 1.5 Edit accept?

Key inputs: `prompt`, `images`, `size`, `background`, `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-1.5-edit.

How long does Gpt Image 1.5 Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 35 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 1.5 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 1.5 Edit | Fast Image Editing API on WaveSpeedAI