Pruna AI P-Image Text to Image is a fast AI image generation model that creates high-quality images from text prompts. Ready-to-use REST inference API for creative design, marketing visuals, concept art, social media content, product mockups, and image generation workflows with simple integration, no coldstarts, and affordable pricing.
Idle

$0.005per run·~200 / $1

A clean studio portrait of an adult US female model against a soft beige background, natural makeup, elegant neutral outfit, calm confident expression, soft diffused studio lighting, premium commercial photography, photorealistic, ultra-detailed, refined composition
Pruna AI P-Image Text-to-Image generates images from natural-language prompts with flexible size controls, custom aspect ratios, and reproducible outputs through seed support. It is suitable for portraits, product visuals, concept art, marketing creatives, and other prompt-driven image generation workflows.
Prompt-based image generation Turn natural-language descriptions into polished images for creative and commercial use.
Flexible aspect ratio control
Use preset aspect ratios or switch to custom for direct width and height control.
Custom image dimensions
Set width and height to better match delivery requirements for web, social, ads, and design workflows.
Multiple output formats
Export generated images as png or other supported formats depending on your workflow.
Seed support for reproducibility
Use seed to reproduce results more consistently across generations.
Affordable fixed pricing Generate images at a simple flat cost per run.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the desired image. |
| aspect_ratio | No | Output aspect ratio. Can use a preset ratio or custom. |
| width | No | Output image width. Only takes effect when aspect_ratio is set to custom. |
| height | No | Output image height. Only takes effect when aspect_ratio is set to custom. |
| output_format | No | Output image format, such as png. |
| seed | No | Random seed for reproducibility. Use the same seed to get more consistent results. |
custom if you want direct control over width and height.aspect_ratio is set to custom.A clean studio portrait of an adult US female model against a soft beige background, natural makeup, elegant neutral outfit, calm confident expression, soft diffused studio lighting, premium commercial photography, photorealistic, ultra-detailed, refined composition
Just $0.005 per image.
custom aspect ratio when you need exact width and height for a delivery format.seed when you want to iterate on a concept with more consistent outputs.png when image quality matters more than file size.prompt is the only required field.width and height only take effect when aspect_ratio is set to custom.seed helps with reproducibility but may not guarantee identical results in every case.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-image/text-to-image with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Image Text To Image below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "custom",
"width": 512,
"height": 512,
"output_format": "png",
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-image/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=$(printf '%s' "$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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/pruna-ai/p-image/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"aspect_ratio": "custom",
"width": 512,
"height": 512,
"output_format": "png",
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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));
}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",
"aspect_ratio": "custom",
"width": 512,
"height": 512,
"output_format": "png",
"seed": -1
}
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/pruna-ai/p-image/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 = 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)P Image Text To Image is a Pruna Ai model for image generation, exposed as a REST API on WaveSpeedAI. Pruna AI P-Image Text to Image is a fast AI image generation model that creates high-quality images from text prompts. Ready-to-use REST inference API for creative design, marketing visuals, concept art, social media content, product mockups, and image generation workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.
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/pruna-ai/pruna-ai-p-image-text-to-image.
P Image Text To Image starts at $0.005 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.
Key inputs: `prompt`, `aspect_ratio`, `seed`, `enable_base64_output`, `enable_sync_mode`, `height`. 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/pruna-ai/pruna-ai-p-image-text-to-image.
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.
Commercial usage rights depend on the model's license, set by its provider (Pruna Ai). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.