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Firered Image Edit

wavespeed-ai /

FireRed Image Edit enables precise image editing with natural-language instructions, supporting both English and Chinese prompts with multi-image references. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

image-to-image
Đầu vào

Chờ

The woman in Figure 1 is walking in the city at night in Figure 2.

$0.08cho mỗi lần chạy·~12 / $1

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Ví dụXem tất cả

The woman in Figure 1 is walking in the city at night in Figure 2.

The woman in Figure 1 is walking in the city at night in Figure 2.

Mô hình liên quan

README

FireRed Image Edit

FireRed Image Edit is a powerful AI image editing model that combines elements from multiple reference images into a single cohesive output. Upload reference images of people, objects, or styles, then describe how to combine them — the model intelligently merges elements while maintaining natural composition and lighting.

Why Choose This?

  • Multi-image composition Combine elements from multiple reference images into one seamless result.

  • Natural language editing Describe your edit in plain text — reference images by Figure number for precise control.

  • Flexible sizing Specify custom output size, or leave blank to match the first image's aspect ratio.

  • Intelligent blending Automatically adapts lighting, perspective, and style for realistic integration.

  • Prompt Enhancer Built-in tool to automatically improve your edit descriptions.

Parameters

ParameterRequiredDescription
promptYesText description of the desired edit (use "Figure 1", "Figure 2" to reference images)
imagesYesReference images to combine (click "+ Add Item" to add more)
sizeNoOutput size; if empty, matches the first image's aspect ratio

How to Use

  1. Upload reference images — add all images containing elements you want to combine.
  2. Write your prompt — describe the edit using "Figure 1", "Figure 2", etc. to reference specific images.
  3. Set size (optional) — specify output dimensions, or leave blank to use first image's ratio.
  4. Use Prompt Enhancer (optional) — click to automatically refine your description.
  5. Run — submit and download your edited image.

Pricing

OutputCost
Per image$0.08

Best Use Cases

  • Virtual Try-On — Put a person in different outfits from reference photos.
  • Character Compositing — Combine character features with new environments or accessories.
  • Product Visualization — Show products on different models or in various settings.
  • Creative Compositing — Merge elements from multiple sources for unique imagery.
  • Marketing & Ads — Create customized visuals by combining brand assets.

Pro Tips

  • Use "Figure 1", "Figure 2", etc. in your prompt to reference images in upload order.
  • Clear, front-facing reference images produce the best element extraction.
  • The first image determines the default output aspect ratio when size is not specified.
  • Describe the scene context (location, lighting, mood) for more cohesive results.
  • For best results, use reference images with similar lighting conditions.

Notes

  • Both prompt and images are required fields.
  • Ensure uploaded image URLs are publicly accessible.
  • Output size defaults to first image's aspect ratio if not specified.
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

Firered Image Edit API — Quick start

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

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/firered-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)

Firered Image Edit API — Frequently asked questions

What is the Firered Image Edit API?

Firered Image Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. FireRed Image Edit enables precise image editing with natural-language instructions, supporting both English and Chinese prompts with multi-image references. 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 Firered 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/firered-image-edit.

How much does Firered Image Edit cost per run?

Firered Image Edit starts at $0.080 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 Firered Image Edit accept?

Key inputs: `prompt`, `images`. 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/firered-image-edit.

How long does Firered Image Edit take to generate?

Median end-to-end generation time on WaveSpeedAI is around 244 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 Firered 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.

Firered Image Edit | Fast Image Editing API | WaveSpeedAI