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Boogu Image Edit API

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Boogu Image Edit transforms input images using English or Chinese edit instructions, supporting prompt-based image editing for visual changes, style adjustments, and image refinements. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

image-to-image
Input

Idle

Turn this image into a high-end swimwear fashion editorial. Keep the woman's identity, swimsuit, pose, and tasteful style consistent. Enhance the lighting into golden hour sunset, add soft ocean reflections, wind moving the linen cover-up, refined cinematic color grading, luxury magazine composition, elegant shadows, and a premium resort atmosphere. Focus on art direction, lighting, fabric movement, and composition rather than sensual posing.

$0.06per run·~16 / $1

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ExamplesView all

Turn this image into a high-end swimwear fashion editorial. Keep the woman's identity, swimsuit, pose, and tasteful style consistent. Enhance the lighting into golden hour sunset, add soft ocean reflections, wind moving the linen cover-up, refined cinematic color grading, luxury magazine composition, elegant shadows, and a premium resort atmosphere. Focus on art direction, lighting, fabric movement, and composition rather than sensual posing.

Turn this image into a high-end swimwear fashion editorial. Keep the woman's identity, swimsuit, pose, and tasteful style consistent. Enhance the lighting into golden hour sunset, add soft ocean reflections, wind moving the linen cover-up, refined cinematic color grading, luxury magazine composition, elegant shadows, and a premium resort atmosphere. Focus on art direction, lighting, fabric movement, and composition rather than sensual posing.

Related Models

README

Boogu Image Edit

Boogu Image Edit transforms an input image using a text instruction. Upload an image, describe the edit you want, and generate an edited image through the standard WaveSpeed prediction response. English and Chinese edit prompts are both supported.

Why Choose This?

  • Instruction-based image editing
    Upload an image and describe the transformation you want in natural language.

  • Bilingual edit prompts
    Use English or Chinese instructions to guide the image edit.

  • Original-ratio default
    When no explicit size is provided, the edited image follows the input image aspect ratio.

  • Simple public interface
    The public form focuses on prompt, input image, and output format.

  • Standard image output
    Edited images are returned as URLs in the standard WaveSpeed prediction response.

Parameters

ParameterRequiredDescription
promptYesEdit instruction describing how to transform the input image. English and Chinese are supported.
imageYesInput image to edit. Upload an image or provide a public image URL.
output_formatNoOutput image format: jpeg or png. Default: jpeg.

How to Use

  1. Upload your image — Provide the input image you want to edit.
  2. Write your edit prompt — Describe what should change and what should stay the same.
  3. Choose output format — Use jpeg for general-purpose output or png when PNG output is needed.
  4. Submit — Generate the edited image and retrieve the output URL.

Pricing

Aspect RatioPrice
Same as input image$0.20
1:1$0.06
16:9$0.04
9:16$0.04
4:3$0.05
3:4$0.05

Best Use Cases

  • Photo transformation — Change backgrounds, style, mood, color, or visual details in an input image.
  • Product and marketing edits — Refine uploaded or generated assets for campaigns, product visuals, and content production.
  • Creative iteration — Try different edit prompts on the same source image.
  • Bilingual workflows — Use English or Chinese instructions in the same API workflow.
  • Format-specific outputs — Generate edited images in jpeg or png depending on the downstream use case.

Pro Tips

  • Use clear edit instructions that describe both what should change and what should remain unchanged.
  • Upload a sharp image with the main subject clearly visible.
  • Keep the prompt focused on one main edit when possible.
  • Use png when you need a higher-quality image for further editing.
  • Use jpeg for smaller, general-purpose edited images.
  • Ensure the input image URL is publicly accessible.
Note:This website uses AI models provided by third parties.

Boogu Image Edit API — Quick start

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

Boogu Image Edit API — Frequently asked questions

What is the Boogu Image Edit API?

Boogu Image Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. Boogu Image Edit transforms input images using English or Chinese edit instructions, supporting prompt-based image editing for visual changes, style adjustments, and image refinements. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Boogu Image 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/wavespeed-ai/boogu-image-edit.

How much does Boogu Image Edit cost per run?

Boogu Image Edit starts at $0.060 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 Boogu Image Edit accept?

Key inputs: `prompt`, `image`, `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/wavespeed-ai/boogu-image-edit.

How long does Boogu Image Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 212 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 Boogu Image Edit outputs commercially?

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

Boogu Image Edit API | WaveSpeedAI