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krea/v3/text-to-image

Krea V3 Text-to-Image generates images from text prompts with selectable quality and speed variants, custom dimensions, and seed control for flexible creative and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Text to Image
Input
width
height
1024 × 1024 px
Range: 256 - 4096

Idle

A beautiful young woman standing alone inside a small flower shop late at night, wearing a dark green cardigan over a cream dress, gently wrapping a bouquet of white lilies and deep red roses in brown paper. Warm hanging lamps illuminate glass vases, scattered petals, handwritten price tags, ribbons, and a sleepy orange cat curled on the counter. Outside the window, light rain reflects city neon. Cinematic realism, rich small details, soft warm lighting, elegant storytelling atmosphere.

$0.03per run·~33 / $1

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ExamplesView all

A beautiful young woman standing alone inside a small flower shop late at night, wearing a dark green cardigan over a cream dress, gently wrapping a bouquet of white lilies and deep red roses in brown paper. Warm hanging lamps illuminate glass vases, scattered petals, handwritten price tags, ribbons, and a sleepy orange cat curled on the counter. Outside the window, light rain reflects city neon. Cinematic realism, rich small details, soft warm lighting, elegant storytelling atmosphere.

A beautiful young woman standing alone inside a small flower shop late at night, wearing a dark green cardigan over a cream dress, gently wrapping a bouquet of white lilies and deep red roses in brown paper. Warm hanging lamps illuminate glass vases, scattered petals, handwritten price tags, ribbons, and a sleepy orange cat curled on the counter. Outside the window, light rain reflects city neon. Cinematic realism, rich small details, soft warm lighting, elegant storytelling atmosphere.

A handsome young man browsing an old outdoor book market in the early morning, wearing a charcoal wool coat, black turtleneck, and leather gloves. He holds an antique book open in one hand while the other hand lifts his round glasses slightly. Around him are weathered books, stacked wooden crates, yellowing maps, fountain pens, and a steaming paper cup of coffee on the table. Soft winter sunlight, cinematic realism, refined details, intellectual and slightly mysterious mood.

A handsome young man browsing an old outdoor book market in the early morning, wearing a charcoal wool coat, black turtleneck, and leather gloves. He holds an antique book open in one hand while the other hand lifts his round glasses slightly. Around him are weathered books, stacked wooden crates, yellowing maps, fountain pens, and a steaming paper cup of coffee on the table. Soft winter sunlight, cinematic realism, refined details, intellectual and slightly mysterious mood.

Related Models

README

Krea V3 Text-to-Image

Krea V3 Text-to-Image generates images from text prompts with custom output dimensions and two generation variants. Use berry for quality-focused generation or jam for faster results.

Why Choose This?

  • Two generation variants — Choose berry for quality or jam for speed.
  • Custom image sizes — Set the output width and height for different layouts.
  • Negative prompt support — Use negative prompts with Berry to reduce unwanted elements.
  • Reasoning control — Choose between low and medium reasoning levels.
  • Seed support — Use a fixed seed for more reproducible results.
  • PNG and JPEG output — Choose the format that fits your workflow.

Parameters

ParameterRequiredDescription
promptYesText description of the image to generate.
modelNoberry for quality-focused generation or jam for faster generation. Default: berry.
negative_promptNoOptional content to avoid. Used only by berry.
sizeNoOutput dimensions in width*height format. Default: 1024*1024.
reasoning_levelNolow or medium. Default: low.
output_formatNopng or jpeg. Default: png.
seedNoOptional integer seed for reproducible generation.

How to Use

  1. Write a prompt — Describe the image you want to generate.
  2. Choose a model — Select berry or jam.
  3. Set image size optional — Enter the desired output dimensions.
  4. Configure optional settings — Add a negative prompt, reasoning level, output format, or seed.
  5. Submit — Generate and retrieve the final image.

Pricing

Pricing is based on the requested output size at $0.03 per megapixel.

Output SizePrice
1024 × 1024$0.03
1024 × 1536$0.045
2048 × 2048$0.12

berry and jam use the same pricing. Other generation settings do not add separate charges.

Best Use Cases

  • Creative image generation — Generate photography, illustrations, and visual concepts.
  • Advertising visuals — Create campaign and promotional imagery.
  • Product concepts — Explore products, materials, and presentation styles.
  • Editorial content — Generate images for articles, covers, and publishing.
  • Custom-format images — Create outputs sized for specific layouts or placements.

Pro Tips

  • Describe the subject, composition, lighting, and visual style clearly.
  • Use berry when quality is the priority.
  • Use jam when faster iteration is preferred.
  • Use negative_prompt with Berry when specific elements should be avoided.
  • Set a fixed seed when comparing prompt changes.

Notes

  • prompt is required.
  • model defaults to berry.
  • size defaults to 1024*1024.
  • Supported dimensions range from 256 to 4096 pixels per side.
  • negative_prompt is used only by Berry.
  • Custom output dimensions are subject to a maximum total pixel count of 4,220,160 pixels. Some width-and-height combinations may be unavailable even when both dimensions are individually within the supported range.

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.

v3 Text To Image API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/krea/v3/text-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 v3 Text 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",
    "size": "1024*1024",
    "reasoning_level": "low",
    "model": "berry",
    "output_format": "png"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/krea/v3/text-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="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/krea/v3/text-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "size": "1024*1024",
        "reasoning_level": "low",
        "model": "berry",
        "output_format": "png"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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",
    "size": "1024*1024",
    "reasoning_level": "low",
    "model": "berry",
    "output_format": "png"
}

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/krea/v3/text-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 = 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)

v3 Text To Image API — Frequently asked questions

What is the v3 Text To Image API?

v3 Text To Image is a Krea model for image generation, exposed as a REST API on WaveSpeedAI. Krea V3 Text-to-Image generates images from text prompts with selectable quality and speed variants, custom dimensions, and seed control for flexible creative and production workflows. 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 v3 Text 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/krea/krea-v3-text-to-image.

How much does v3 Text To Image cost per run?

v3 Text To Image starts at $0.03 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 v3 Text To Image accept?

Key inputs: `prompt`, `size`, `seed`, `negative_prompt`, `model`, `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/krea/krea-v3-text-to-image.

How do I get started with the v3 Text To Image 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 v3 Text To Image outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Krea). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

llms.txt — krea/v3/text-to-image for AI agents and LLMs