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Reve 2.1 Image Edit transforms existing images with natural-language edit instructions, preserving layout intelligence, accurate text rendering, and fine visual details for posters, ads, product visuals, and typography-rich designs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
Input

Idle

Transform this cat portrait into a magical forest adventure poster. Keep the cat's face, fur color, body pose, and expression consistent. Replace the simple background with a moonlit enchanted forest, glowing mushrooms, tiny floating fireflies, soft blue mist, and cinematic fantasy lighting. Add a subtle golden glow around the cat's eyes while keeping it elegant and realistic.

$0.28per run·~35 / $10

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Transform this cat portrait into a magical forest adventure poster. Keep the cat's face, fur color, body pose, and expression consistent. Replace the simple background with a moonlit enchanted forest, glowing mushrooms, tiny floating fireflies, soft blue mist, and cinematic fantasy lighting. Add a subtle golden glow around the cat's eyes while keeping it elegant and realistic.

Transform this cat portrait into a magical forest adventure poster. Keep the cat's face, fur color, body pose, and expression consistent. Replace the simple background with a moonlit enchanted forest, glowing mushrooms, tiny floating fireflies, soft blue mist, and cinematic fantasy lighting. Add a subtle golden glow around the cat's eyes while keeping it elegant and realistic.

Related Models

README

Reve 2.1 Edit

Reve 2.1 Edit applies natural-language instructions to an existing image. It is designed for precise visual changes, layout-aware recomposition, and detailed image refinement while preserving the parts of the source image that should remain unchanged.

Why Choose This?

  • Instruction-based image editing
    Edit an existing image by describing the desired change in natural language.

  • Precise visual refinement
    Apply focused changes to objects, backgrounds, colors, lighting, layout, or style.

  • Preservation-aware editing
    Keep important parts of the original image unchanged while modifying selected details.

  • Layout-aware recomposition
    Adjust composition and visual structure while maintaining a coherent final image.

  • Flexible output formats
    Export edited images as png, jpeg, or webp.

Parameters

ParameterRequiredDescription
promptYesText description of how to edit the reference image.
imageYesReference image to edit. Public image URLs and uploaded images are supported.
aspect_ratioNoOutput aspect ratio, including auto and supported ratio presets.
output_formatNoOutput format: png, jpeg, or webp.

How to Use

  1. Upload your image — Provide the source image you want to edit.
  2. Write your edit prompt — Describe the desired changes and clearly state what should remain unchanged.
  3. Choose aspect ratio — Select an aspect ratio or use auto to let the model choose the best layout.
  4. Choose output format — Select png, jpeg, or webp when needed.
  5. Submit — Generate the edited image and retrieve the output URL.

Pricing

OutputPrice
One image$0.28

Best Use Cases

  • Background editing — Replace, clean up, or restyle image backgrounds.
  • Object changes — Remove, replace, recolor, or refine objects in an image.
  • Product image refinement — Improve product visuals for marketing, ecommerce, and presentation.
  • Layout adjustments — Recompose images while keeping the main subject and visual intent intact.
  • Portrait and scene edits — Adjust style, lighting, mood, or environment in existing images.

Pro Tips

  • Describe both the change and the elements that must stay unchanged.
  • Use concise, concrete instructions for object removal, replacement, color, lighting, and background edits.
  • Use a clear source image with the main subject visible.
  • Use png for high-quality general output, jpeg for smaller files, and webp for web-friendly images.
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.

2.1 Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/reve/2.1/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 2.1 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",
    "aspect_ratio": "4:1",
    "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/reve/2.1/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/reve/2.1/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "aspect_ratio": "4:1",
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "aspect_ratio": "4:1",
    "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/reve/2.1/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)

2.1 Edit API — Frequently asked questions

What is the 2.1 Edit API?

2.1 Edit is a Reve model for image editing, exposed as a REST API on WaveSpeedAI. Reve 2.1 Image Edit transforms existing images with natural-language edit instructions, preserving layout intelligence, accurate text rendering, and fine visual details for posters, ads, product visuals, and typography-rich designs. 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 2.1 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/reve/reve-2.1-edit.

How much does 2.1 Edit cost per run?

2.1 Edit starts at $0.28 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 2.1 Edit accept?

Key inputs: `prompt`, `image`, `aspect_ratio`, `enable_base64_output`, `enable_sync_mode`, `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/reve/reve-2.1-edit.

How long does 2.1 Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 62 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 2.1 Edit outputs commercially?

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

Reve 2.1 Image Edit API on WaveSpeedAI