P-H3-Image Edit API

wavespeed-ai/p-h3-image/image-edit

P-H3-Image by Pruna AI, built on the MiniMax H3 open weights, edits an image from a text instruction with up to 8 extra reference images, keeping faces, products and details consistent, at 1K to 4K resolution. Ready-to-use REST inference API, best performance, no cold starts, affordable pricing.

Image Editing
Input

Drag & drop or click to upload

preview

Idle

Transform her into a glamorous 1970s rock star wearing a metallic silver jumpsuit and oversized tinted sunglasses, standing backstage before a huge concert with colorful stage lights and vintage amplifiers. Keep her face and braided blonde hair unchanged.

$0.03per run·~33 / $1

ExamplesView all

Transform her into a glamorous 1970s rock star wearing a metallic silver jumpsuit and oversized tinted sunglasses, standing backstage before a huge concert with colorful stage lights and vintage amplifiers. Keep her face and braided blonde hair unchanged.

Transform her into a glamorous 1970s rock star wearing a metallic silver jumpsuit and oversized tinted sunglasses, standing backstage before a huge concert with colorful stage lights and vintage amplifiers. Keep her face and braided blonde hair unchanged.

The woman from <Picture 1> and the man from <Picture 2> sitting together in a booth of a retro American diner at night, sharing a milkshake, both keeping their exact faces and hair. Photorealistic, warm neon light.

The woman from <Picture 1> and the man from <Picture 2> sitting together in a booth of a retro American diner at night, sharing a milkshake, both keeping their exact faces and hair. Photorealistic, warm neon light.

Related Models

README

P-H3-Image Edit

P-H3-Image is Pruna AI's image generation and editing model built on the MiniMax H3 open weights. This endpoint edits the first input image from a text instruction, using up to 8 more images as references, and renders the result from 1K up to 4K.

Powered by MiniMax H3.

Why Choose This?

  • Photorealistic by default
    Natural skin, light and materials, with strong prompt adherence for documentary, editorial and cinematic looks.

  • Identity and product consistency
    Faces, hairstyles, outfits and product details carry over from the input images.

  • Up to 4K output
    Choose 1k, 1.5k, 2k, 3k or 4k; 3k sits closest to the model's sweet spot for fine detail.

  • Built-in prompt enhancement
    Short requests are expanded into detailed prompts automatically, so a one-line idea still renders well.

  • Multi-image references
    Combine up to 9 images, addressed in the prompt as <Picture 1>, <Picture 2>, and so on.

Parameters

ParameterRequiredDescription
promptYesThe edit instruction. Refer to the input images as <Picture 1> .. <Picture N>.
imagesYes1 to 9 image URLs. The first is the image being edited; the rest are references.
aspect_ratioNoOutput aspect ratio: 1:1, 1:2, 2:1, 1:3, 3:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 9:21, 21:9. Defaults to the first image's aspect ratio.
resolutionNoOutput resolution: 1k (default, ~1 MP), 1.5k (~2.4 MP), 2k (~4 MP), 3k (~9.4 MP) or 4k (~16.8 MP).
output_formatNojpeg (default), png or webp.
seedNoRandom seed. -1 picks a random seed.

How to Use

  1. Upload images — The image to edit first; add references for outfits, products or more people.
  2. Describe the result — Say what should change and what should stay, e.g. Put her in a sunflower field at golden hour wearing a wide straw hat; keep her face and braided hair.
  3. Choose resolution — 1k–2k for iteration, 3k or 4k for final assets.
  4. Submit — Receive the edited image URL.

Pricing

Pricing is based on the selected resolution.

The prices below include the first input image. Each additional input image costs $0.005, regardless of resolution.

ResolutionCost per image
1k$0.03
1.5k$0.06
2k$0.09
3k$0.15
4k$0.24

Pro Tips

  • Describe the scene concretely (subject, setting, light, lens); avoid hype words, which tend to make images worse.
  • Name what each reference is for, e.g. the hat from <Picture 2>.
  • Short quoted text such as a sign or a title renders well; long or small text is less reliable.
  • Use 3k or 4k for hands, small background figures and fine textures.
  • Fix the seed to iterate on wording without changing the composition.

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.

P H3 Image Image Edit API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/p-h3-image/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 P H3 Image 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"
    ],
    "aspect_ratio": "1:1",
    "resolution": "1k",
    "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/p-h3-image/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="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/p-h3-image/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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "aspect_ratio": "1:1",
        "resolution": "1k",
        "output_format": "jpeg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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"
    ],
    "aspect_ratio": "1:1",
    "resolution": "1k",
    "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/p-h3-image/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 = 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)

P H3 Image Image Edit API — Frequently asked questions

What is the P H3 Image Image Edit API?

P H3 Image Image Edit is a WaveSpeedAI model for image editing, exposed as a REST API on WaveSpeedAI. P-H3-Image by Pruna AI, built on the MiniMax H3 open weights, edits an image from a text instruction with up to 8 extra reference images, keeping faces, products and details consistent, at 1K to 4K resolution. 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 P H3 Image 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 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/p-h3-image-image-edit.

How much does P H3 Image Image Edit cost per run or per second?

P H3 Image Image Edit starts at $0.03 per run or per second. 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 P H3 Image Image Edit accept?

Key inputs: `prompt`, `images`, `aspect_ratio`, `resolution`, `seed`, `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/wavespeed-ai/p-h3-image-image-edit.

How do I get started with the P H3 Image Image Edit API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use P H3 Image Image 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/p-h3-image/image-edit for AI agents and LLMs