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wavespeed-ai/qwen-image-2.0/edit

Qwen Image 2.0 Edit is an advanced image-editing model with improved quality and better understanding of instructions. Up to 2k. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Image Editing
Input

Drag & drop or click to upload

preview

Drag & drop or click to upload

preview

Image dimensions in width*height format (e.g., 1024*1024, 1280*720)

Idle

STYLE: Emphasize photographic qualities like camera settings, depth of field, and lighting.  
A candid, high-resolution group photo captured with a wide-angle lens, shallow depth of field, and natural ambient lighting. The individuals in Figure 1 and Figure 2 are positioned in a tight, casual formation, smiling slightly or looking toward the camera with relaxed expressions. Their clothing and background suggest a real-world, outdoor setting with soft bokeh and diffused daylight. The composition is centered, with natural depth of field emphasizing the group while blurring the periphery. Shot on film simulation with subtle grain, warm color grading, and realistic lens distortion to enhance authenticity.

$0.03per run·~33 / $1

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ExamplesView all

STYLE: Emphasize photographic qualities like camera settings, depth of field, and lighting.  
A candid, high-resolution group photo captured with a wide-angle lens, shallow depth of field, and natural ambient lighting. The individuals in Figure 1 and Figure 2 are positioned in a tight, casual formation, smiling slightly or looking toward the camera with relaxed expressions. Their clothing and background suggest a real-world, outdoor setting with soft bokeh and diffused daylight. The composition is centered, with natural depth of field emphasizing the group while blurring the periphery. Shot on film simulation with subtle grain, warm color grading, and realistic lens distortion to enhance authenticity.

STYLE: Emphasize photographic qualities like camera settings, depth of field, and lighting. A candid, high-resolution group photo captured with a wide-angle lens, shallow depth of field, and natural ambient lighting. The individuals in Figure 1 and Figure 2 are positioned in a tight, casual formation, smiling slightly or looking toward the camera with relaxed expressions. Their clothing and background suggest a real-world, outdoor setting with soft bokeh and diffused daylight. The composition is centered, with natural depth of field emphasizing the group while blurring the periphery. Shot on film simulation with subtle grain, warm color grading, and realistic lens distortion to enhance authenticity.

STYLE: Emphasize photographic qualities like camera settings, depth of field, and lighting.
A vibrant, sunlit beach scene captured with a wide-angle lens, shallow depth of field, and natural golden-hour lighting. A young man in casual beachwear—linen short-sleeve shirt left slightly unbuttoned, rolled-up chino shorts, and leather flip-flops—stands barefoot on soft sand, smiling confidently at the camera. His hair is lightly tousled by the sea breeze, with a pair of aviator sunglasses resting on his head, and his expression radiates ease and carefree energy. The background features blurred turquoise waves and distant palm trees, creating a dreamy bokeh effect. Shot in candid-style beach photography with warm tones, high dynamic range, and realistic textures. Medium shot, eye-level, capturing his full upper body and surroundings with a natural, inviting atmosphere.

STYLE: Emphasize photographic qualities like camera settings, depth of field, and lighting. A vibrant, sunlit beach scene captured with a wide-angle lens, shallow depth of field, and natural golden-hour lighting. A young man in casual beachwear—linen short-sleeve shirt left slightly unbuttoned, rolled-up chino shorts, and leather flip-flops—stands barefoot on soft sand, smiling confidently at the camera. His hair is lightly tousled by the sea breeze, with a pair of aviator sunglasses resting on his head, and his expression radiates ease and carefree energy. The background features blurred turquoise waves and distant palm trees, creating a dreamy bokeh effect. Shot in candid-style beach photography with warm tones, high dynamic range, and realistic textures. Medium shot, eye-level, capturing his full upper body and surroundings with a natural, inviting atmosphere.

Move the dancer onto a grand theater stage during a performance. Keep her pose and grey leotard, but add a dramatic single spotlight from above, dark velvet curtains, floating dust in the light beam and a soft haze, cinematic stage photography.

Move the dancer onto a grand theater stage during a performance. Keep her pose and grey leotard, but add a dramatic single spotlight from above, dark velvet curtains, floating dust in the light beam and a soft haze, cinematic stage photography.

Turn this photo into a food magazine cover. Keep the pancakes as the hero image, add the magazine title "SUNDAY BRUNCH" in bold serif at the top, and cover lines "50 Easy Weekend Recipes" and "The Perfect Fluffy Stack" on the side, clean modern layout.

Turn this photo into a food magazine cover. Keep the pancakes as the hero image, add the magazine title "SUNDAY BRUNCH" in bold serif at the top, and cover lines "50 Easy Weekend Recipes" and "The Perfect Fluffy Stack" on the side, clean modern layout.

Place the fennec fox from Figure 1 sitting on the grassy cliff in front of the lighthouse from Figure 2, looking out at the ocean at sunset, warm golden light and long shadows, photorealistic.

Place the fennec fox from Figure 1 sitting on the grassy cliff in front of the lighthouse from Figure 2, looking out at the ocean at sunset, warm golden light and long shadows, photorealistic.

Related Models

README

Qwen Image 2.0 Edit

Qwen Image 2.0 Edit is an image-editing model for transforming existing images with natural-language instructions. Upload 1 to 3 reference images, describe the edit you want, and generate a refined image while preserving the visual context from your inputs.

Why Choose This?

  • Instruction-based image editing Modify, restyle, or enhance uploaded images using a simple text prompt.

  • Multi-image input Supports up to 3 input images for edits, references, or visual context.

  • Strong prompt understanding Follows detailed Chinese or English editing instructions for targeted changes.

  • High-resolution workflow Supports images from 384 to 3072 pixels on each dimension, with output up to 2k.

  • Reproducible results Set a seed when you need repeatable generations.

Parameters

ParameterRequiredDescription
promptYesText instruction describing the desired edit. Supports Chinese and English, up to 800 characters.
imagesYesInput images for editing. Upload 1 to 3 images, each 384-3072px per dimension.
seedNoRandom seed for reproducibility (-1 for random, 0-2147483647 for a fixed seed).

How to Use

  1. Upload your image inputs - add 1 to 3 images to edit or use as references.
  2. Write your prompt - describe the change, style, object, background, or composition you want.
  3. Set seed (optional) - use a fixed seed for reproducible results.
  4. Run - submit the request and download the generated image.

Pricing

OutputCost
Per image edit$0.03

Best Use Cases

  • Photo retouching - Adjust appearance, lighting, composition, or scene details.
  • Creative edits - Transform style, mood, clothing, backgrounds, or objects.
  • Product visuals - Refine product shots or create alternate presentation styles.
  • Character and portrait edits - Preserve identity while changing details or aesthetics.
  • Reference-guided edits - Use multiple images to provide context for the final output.

Pro Tips

  • Use clear, specific edit instructions instead of broad prompts.
  • Upload only the images needed for the edit; the model supports a maximum of 3 input images.
  • Mention what should stay unchanged when preservation matters.
  • Use a fixed seed when comparing prompt variations.
  • For final production quality, use the Pro model; for lower-cost iteration, use the standard model.

Notes

  • prompt and images are required.
  • images accepts 1 to 3 images.
  • Each input image should be 384-3072px on each dimension.
  • Ensure image URLs are publicly accessible when using URL inputs.
  • Ensure your prompts and images comply with content guidelines.

Related Models

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.

Qwen Image 2.0 Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image-2.0/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 Qwen Image 2.0 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"
    ]
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/qwen-image-2.0/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/wavespeed-ai/qwen-image-2.0/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ]
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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"
    ]
}

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/qwen-image-2.0/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)

Qwen Image 2.0 Edit API — Frequently asked questions

What is the Qwen Image 2.0 Edit API?

Qwen Image 2.0 Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. Qwen Image 2.0 Edit is an advanced image-editing model with improved quality and better understanding of instructions. Up to 2k. 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 Qwen Image 2.0 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/wavespeed-ai/qwen-image-2.0-edit.

How much does Qwen Image 2.0 Edit cost per run?

Qwen Image 2.0 Edit starts at $0.03 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 Qwen Image 2.0 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/qwen-image-2.0-edit.

How long does Qwen Image 2.0 Edit take to generate?

Reported generation time on WaveSpeedAI is around 11 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 Qwen Image 2.0 Edit outputs commercially?

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

llms.txt — wavespeed-ai/qwen-image-2.0/edit for AI agents and LLMs