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Molmo2 Prompt Optimizer

wavespeed-ai /

Molmo2-4B Prompt Optimizer: Enhance prompts for image and video generation with intelligent restructuring, style guidance, and context-aware improvements. Open-source vision-language model. Ready-to-use REST API, no cold starts, affordable pricing.

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Ocioso

A vibrant, swirling painting in the style of Vincent van Gogh's "Starry Night." The night sky is filled with dynamic, swirling patterns in shades of blue, yellow, and white, with a prominent crescent moon in the upper right corner. Below, a small town with blue and green buildings, including a church with a tall steeple, is nestled among rolling hills and mountains. The scene is illuminated by warm yellow lights from the windows, creating a striking contrast against the cool night sky. The overall composition is rich in texture and movement, capturing the essence of van Gogh's post-impressionist style.

$0.003por execução·~333 / $1

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README

Molmo2 Prompt Optimizer

Enhance your prompts for better AI generation results with Molmo2 Prompt Optimizer. This intelligent tool analyzes your input — whether text or image — and generates optimized prompts tailored for different styles and output modes. Perfect for improving text-to-image and text-to-video generation quality.

Why It Works Great

  • Dual input modes: Analyze images or text to generate optimized prompts.
  • Multiple styles: Optimize for artistic, photographic, technical, anime, or realistic output.
  • Image & video support: Generate prompts optimized for either image or video generation.
  • Instant results: Fast processing for seamless workflow integration.
  • Ultra-affordable: Just $0.003 per optimization — 333 runs for $1.
  • Quality boost: Get better results from your generation models.

Parameters

ParameterRequiredDescription
imageNoReference image to analyze and describe.
textNoText prompt to enhance and optimize.
styleNoOutput style: default, artistic, photographic, technical, anime, or realistic.
modeNoTarget generation type: image or video. Default: image.

How to Use

From Image

  1. Upload an image — the image you want to describe or recreate.
  2. Select style — choose the aesthetic direction.
  3. Select mode — image or video generation target.
  4. Run — get an optimized prompt describing the image.

From Text

  1. Enter your prompt — your basic idea or description.
  2. Select style — choose the aesthetic direction.
  3. Select mode — image or video generation target.
  4. Run — get an enhanced, detailed prompt.

Pricing

Flat rate per optimization.

OutputCost
Per optimization$0.003
100 optimizations$0.30
1,000 optimizations$3.00

Style Guide

StyleDescriptionBest For
defaultBalanced, general-purpose optimizationAny content type
artisticCreative, expressive, painterly languageArt, illustrations, creative work
photographicCamera, lens, and lighting terminologyPhotos, portraits, products
technicalPrecise, detailed specificationsTechnical diagrams, precise output
animeJapanese animation style keywordsAnime characters, manga art
realisticPhotorealistic, lifelike descriptionsRealistic renders, simulations

Mode Options

ModeDescription
imageOptimizes prompts for text-to-image models
videoOptimizes prompts for text-to-video models with motion descriptions

Best Use Cases

  • Image Captioning — Generate detailed prompts from reference images.
  • Prompt Enhancement — Upgrade basic prompts to detailed descriptions.
  • Style Conversion — Reframe prompts for different aesthetic styles.
  • Cross-model Optimization — Adapt prompts for image vs video generation.
  • Batch Processing — Optimize many prompts affordably at scale.
  • Learning Tool — Understand what makes effective generation prompts.

Pro Tips for Best Results

  • Use image input to reverse-engineer prompts from existing artwork.
  • Match style to your target model's strengths (anime for anime models, etc.).
  • Switch mode to "video" when targeting video generation for motion-aware prompts.
  • Combine: upload image + add text for context-aware optimization.
  • At $0.003 per run, experiment freely to find optimal prompt styles.

Notes

  • Provide either image or text (or both) as input.
  • Processing is near-instant for rapid iteration.
  • Video mode adds motion and temporal descriptions to prompts.
  • Results can be directly used with generation models on WaveSpeed.
Nota:Este site utiliza modelos de IA fornecidos por terceiros.

Molmo2 Prompt Optimizer API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/prompt-optimizer 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 Molmo2 Prompt Optimizer below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "style": "default",
    "mode": "image"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/molmo2/prompt-optimizer" \
  -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/molmo2/prompt-optimizer";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "style": "default",
        "mode": "image"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "style": "default",
    "mode": "image"
}

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/molmo2/prompt-optimizer", 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)

Molmo2 Prompt Optimizer API — Frequently asked questions

What is the Molmo2 Prompt Optimizer API?

Molmo2 Prompt Optimizer is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Molmo2-4B Prompt Optimizer: Enhance prompts for image and video generation with intelligent restructuring, style guidance, and context-aware improvements. Open-source vision-language model. Ready-to-use REST API, no cold starts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Molmo2 Prompt Optimizer 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/molmo2-prompt-optimizer.

How much does Molmo2 Prompt Optimizer cost per run?

Molmo2 Prompt Optimizer starts at $0.003 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 Molmo2 Prompt Optimizer accept?

Key inputs: `image`, `enable_sync_mode`, `mode`, `style`, `text`. 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/molmo2-prompt-optimizer.

How long does Molmo2 Prompt Optimizer take to generate?

Median end-to-end generation time on WaveSpeedAI is around 6 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 Molmo2 Prompt Optimizer 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.

Molmo2 Prompt Optimizer | Fast LLM API | WaveSpeedAI