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Wan 2.2 Image to Image

wavespeed-ai /

WAN 2.2 (14B) is an image-to-image model for high-resolution photorealistic image editing with exceptional precision and fidelity. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
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就緒

Convert to Japanese anime style with vivid colors, exaggerated lighting, and stylized raindrops

$0.02每次運行·~50 / $1

下一步:

示例查看全部

Convert to Japanese anime style with vivid colors, exaggerated lighting, and stylized raindrops

Convert to Japanese anime style with vivid colors, exaggerated lighting, and stylized raindrops

Convert to cinematic rainy forest scene with soft mist and dramatic lighting

Convert to cinematic rainy forest scene with soft mist and dramatic lighting

Transform into fantasy world with floating islands and dragon flying in the distance

Transform into fantasy world with floating islands and dragon flying in the distance

Turn into a night scene with snow-covered peaks and aurora borealis glowing in the sky

Turn into a night scene with snow-covered peaks and aurora borealis glowing in the sky

Change to post-apocalyptic ruin with vines, cracks, and smoke in the sky

Change to post-apocalyptic ruin with vines, cracks, and smoke in the sky

Convert into abstract painting with distorted colors and flowing shapes, surreal art style

Convert into abstract painting with distorted colors and flowing shapes, surreal art style

Transform her into a futuristic android with metallic textures, LED patterns, and sci-fi background

Transform her into a futuristic android with metallic textures, LED patterns, and sci-fi background

Reimagine as an ancient wooden warship sailing through foggy sea, cinematic lighting

Reimagine as an ancient wooden warship sailing through foggy sea, cinematic lighting

Convert to surreal dreamscape with floating buildings, inverted reflections, and glowing skies

Convert to surreal dreamscape with floating buildings, inverted reflections, and glowing skies

A young woman wearing a black T-shirt with the word "WaveSpeedAI" printed on the front in modern white font

A young woman wearing a black T-shirt with the word "WaveSpeedAI" printed on the front in modern white font

相關模型

README

Wan 2.2 Image-to-Image

Wan 2.2 Image-to-Image is a versatile image transformation model that modifies existing images based on text prompts. Convert photos to different styles, apply artistic effects, or reimagine scenes while preserving the original composition and structure.

Why It Stands Out

  • Style transformation: Convert images to different artistic styles like anime, oil painting, or photorealistic renders.
  • Prompt-guided editing: Describe the changes you want and watch the image transform.
  • Prompt Enhancer: Built-in AI-powered prompt optimization for better transformation results.
  • Strength control: Fine-tune how much the original image is preserved versus transformed.
  • Flexible resolution: Customize width and height for your desired output size.
  • Multiple output formats: Export as JPEG, PNG, or other formats.
  • Reproducibility: Use the seed parameter to recreate exact results.

Parameters

ParameterRequiredDescription
promptYesText description of the transformation you want.
imageYesSource image (upload or public URL).
strengthNoHow much to transform the image (0.0–1.0, default: 0.6).
widthNoOutput width in pixels (default: 1024).
heightNoOutput height in pixels (default: 1024).
seedNoSet for reproducibility; -1 for random.
output_formatNoOutput format: jpeg, png, etc. (default: jpeg).
enable_base64_outputNoReturn base64 string instead of URL (API only).
enable_sync_modeNoWait for result before returning response (API only).

How to Use

  1. Upload your source image — drag and drop a file or paste a public URL.
  2. Write a prompt describing the transformation you want. Use the Prompt Enhancer for AI-assisted optimization.
  3. Adjust strength — lower values (0.2–0.4) preserve more of the original; higher values (0.6–0.9) allow more dramatic changes.
  4. Set dimensions — adjust width and height as needed.
  5. Click Run and download your transformed image.

Best Use Cases

  • Style Transfer — Convert photos to anime, watercolor, sketch, or other artistic styles.
  • Photo Enhancement — Apply cinematic lighting, color grading, or atmospheric effects.
  • Creative Reimagining — Transform scenes into different seasons, times of day, or moods.
  • Content Creation — Generate stylized versions of images for social media and marketing.
  • Concept Art — Quickly explore visual variations of reference images.

Pricing

OutputPrice
Per image$0.02

Pro Tips for Best Quality

  • Use lower strength (0.3–0.5) to preserve more details from the original image.
  • Use higher strength (0.6–0.8) for dramatic style changes like photo-to-anime conversion.
  • Be specific in your prompt — describe the style, lighting, colors, and mood you want.
  • Start with default strength and adjust based on results.
  • Fix the seed when iterating to compare different prompt variations.

Notes

  • Ensure uploaded image URLs are publicly accessible.
  • Processing time varies based on resolution and current queue load.
  • Please ensure your prompts comply with content guidelines.
提示:本網站部分功能由第三方 AI 模型提供支援。

Wan 2.2 Image To Image API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/wan-2.2/image-to-image 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 Wan 2.2 Image To Image 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",
    "strength": 0.6,
    "size": "1024*1024",
    "seed": -1,
    "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/wan-2.2/image-to-image" \
  -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/wan-2.2/image-to-image";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "strength": 0.6,
        "size": "1024*1024",
        "seed": -1,
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "strength": 0.6,
    "size": "1024*1024",
    "seed": -1,
    "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/wan-2.2/image-to-image", 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)

Wan 2.2 Image To Image API — Frequently asked questions

What is the Wan 2.2 Image To Image API?

Wan 2.2 Image To Image is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. WAN 2.2 (14B) is an image-to-image model for high-resolution photorealistic image editing with exceptional precision and fidelity. 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 Wan 2.2 Image To Image 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/wan-2.2-image-to-image.

How much does Wan 2.2 Image To Image cost per run?

Wan 2.2 Image To Image starts at $0.020 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 Wan 2.2 Image To Image accept?

Key inputs: `prompt`, `image`, `size`, `seed`, `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/wavespeed-ai/wan-2.2-image-to-image.

How long does Wan 2.2 Image To Image take to generate?

Median end-to-end generation time on WaveSpeedAI is around 48 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 Wan 2.2 Image To Image 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.

Wan 2.2 Image to Image | Fast Image Editing API | WaveSpeedAI