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Hunyuan Image 3 Instruct Edit

wavespeed-ai /

Hunyuan Image 3.0 Instruct Edit – instruction-based image editing with natural language prompts, supporting up to 2 reference images for precise modifications. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

image-to-image
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Bereit

Change the background to a sleek, professional photography studio with clean white walls, and the man is looking at the camera.

$0.12pro Durchlauf·~83 / $10

Weiter:

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Change the background to a sleek, professional photography studio with clean white walls, and the man is looking at the camera.

Change the background to a sleek, professional photography studio with clean white walls, and the man is looking at the camera.

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README

Hunyuan Image 3 Instruct Edit

Hunyuan Image 3 Instruct Edit is Tencent's advanced image editing model that transforms existing images based on text instructions. Upload your source images and describe the changes you want — the model intelligently edits while preserving the original style and composition.

Why Choose This?

  • Text-driven editing Modify images using natural language instructions for intuitive control.

  • Multi-image input Support for multiple reference images to guide complex edits.

  • Context-aware modifications Understands scene structure and object relationships for coherent edits.

  • Multiple aspect ratios Preset options for 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, and 2:3.

  • Flexible sizing Custom width and height from 256 to 1536 pixels.

  • Prompt Enhancer Built-in tool to automatically improve your editing instructions.

Parameters

ParameterRequiredDescription
promptYesText instruction describing the desired edit
imagesYesSource images to edit (click "+ Add Item" for multiple)
sizeNoPreset aspect ratio: 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3
widthNoOutput width in pixels (256-1536, default: 256)
heightNoOutput height in pixels (256-1536, default: 256)
seedNoRandom seed for reproducibility

How to Use

  1. Upload your images — add one or more source images by clicking "+ Add Item".
  2. Write your prompt — describe the edits you want (add, remove, modify elements).
  3. Select size preset — choose an aspect ratio that fits your needs.
  4. Adjust dimensions (optional) — fine-tune width and height if needed.
  5. Set seed (optional) — use a fixed seed for reproducible results.
  6. Run — submit and download your edited image.

Pricing

OutputCost
Per image$0.12

Best Use Cases

  • Photo Retouching — Remove unwanted objects, fix imperfections, enhance details.
  • Creative Editing — Transform scenes, change backgrounds, add artistic elements.
  • Product Photography — Modify product images, change colors, adjust compositions.
  • Content Creation — Adapt existing visuals for different contexts and platforms.
  • Design Iteration — Quickly explore variations of existing artwork.

Pro Tips

  • Use clear, specific instructions for best results (e.g., "remove the person in the background" instead of "clean up the image").
  • Upload high-quality source images for better editing results.
  • Use the Prompt Enhancer to refine your editing instructions.
  • Multiple images can provide additional context for complex edits.
  • Keep the same seed when comparing different edit instructions.

Notes

  • Both prompt and images are required fields.
  • Ensure uploaded image URLs are publicly accessible.
  • Resolution range is 256-1536 pixels for both width and height.
  • For best quality, match output dimensions to your source image aspect ratio.

Related Models

Hinweis:Diese Website nutzt KI-Modelle von Drittanbietern.

Hunyuan Image 3 Instruct Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan-image-3-instruct/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 Hunyuan Image 3 Instruct 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"
    ],
    "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan-image-3-instruct/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/hunyuan-image-3-instruct/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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"
    ],
    "seed": -1
}

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/hunyuan-image-3-instruct/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)

Hunyuan Image 3 Instruct Edit API — Frequently asked questions

What is the Hunyuan Image 3 Instruct Edit API?

Hunyuan Image 3 Instruct Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. Hunyuan Image 3.0 Instruct Edit – instruction-based image editing with natural language prompts, supporting up to 2 reference images for precise modifications. 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 Hunyuan Image 3 Instruct 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/hunyuan-image-3-instruct-edit.

How much does Hunyuan Image 3 Instruct Edit cost per run?

Hunyuan Image 3 Instruct Edit starts at $0.12 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 Hunyuan Image 3 Instruct Edit accept?

Key inputs: `prompt`, `images`, `seed`. 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/hunyuan-image-3-instruct-edit.

How long does Hunyuan Image 3 Instruct Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 77 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 Hunyuan Image 3 Instruct 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.

Hunyuan Image 3 Instruct Edit | Fast Image Editing API | WaveSpeedAI