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Pruna AI P-Image Text to Image Trainer is a fast AI model training workflow for customizing text-to-image generation models with user-provided data. Ready-to-use REST inference API for training custom styles, brand-specific visuals, character concepts, product image generation, marketing creatives, and personalized AI image workflows with simple integration, no coldstarts, and affordable pricing.

training
Input

Idle

$1.8per run

Related Models

README

Pruna AI P-Image Text-to-Image Trainer

Pruna AI P-Image Text-to-Image Trainer is a fast training workflow for creating custom LoRAs for the Pruna text-to-image ecosystem. Upload your training image data, optionally provide a default caption, choose the number of training steps, and generate a LoRA that can be used to steer future image generation toward your style, subject, or brand look.

Why Choose This?

  • Fast custom LoRA training Train a text-to-image LoRA for specialized styles, subjects, or branded visuals.

  • Simple training interface Provide your training image data, optional default caption, and training steps without a complex setup process.

  • Optional caption guidance Use default_caption to provide consistent text conditioning across the training dataset.

  • Flexible training depth Use steps to balance speed, cost, and how strongly the LoRA learns your dataset.

  • Built for the Pruna image stack Trained outputs are intended for downstream use with Pruna text-to-image LoRA workflows.

  • Production-ready API Suitable for custom style pipelines, branded asset generation, and repeatable image workflow customization.

Parameters

ParameterRequiredDescription
image_dataYesTraining image data used to create the LoRA.
default_captionNoOptional default caption applied across the training workflow for more consistent conditioning.
stepsNoNumber of training steps. Higher values generally increase training time and cost. Default: 101.

How to Use

  1. Upload your training data — provide the image dataset you want to use for training.
  2. Add a default caption (optional) — use a short caption if you want more consistent text conditioning across the dataset.
  3. Set training steps — choose how many steps to run based on your desired balance of speed and training strength.
  4. Submit — start the training job.
  5. Use the trained LoRA — apply the resulting LoRA in downstream Pruna text-to-image LoRA workflows.

Example Workflow

Train a custom style LoRA from a curated image set, optionally using a shared default caption, then use the resulting weights in Pruna AI P-Image Text-to-Image LoRA for generation.

Pricing

Pricing is based on the selected steps value.

StepsCost
100$0.18
101$0.1818
250$0.45
500$0.90
1000$1.80
2000$3.60

Billing Rules

  • Pricing scales linearly with steps
  • Cost is $1.80 per 1,000 steps
  • Higher steps values increase total training cost proportionally
  • default_caption does not affect pricing

Best Use Cases

  • Custom style training — Create LoRAs for a distinct visual style or art direction.
  • Brand consistency — Train reusable LoRAs for campaigns, products, or branded aesthetics.
  • Subject-focused generation — Teach the model a recurring character, fashion look, or visual concept.
  • Creative workflow personalization — Build specialized LoRAs for repeatable prompt-driven generation.
  • Marketing asset pipelines — Create tailored generation tools for ongoing content production.

Pro Tips

  • Use a clean, consistent training dataset for better LoRA quality.
  • Add a default_caption when your dataset shares a common concept, subject, or style cue.
  • Start with a moderate number of steps before pushing to larger training runs.
  • Increase steps gradually if the initial LoRA is too weak or underfit.
  • Keep your dataset focused on the style or subject you want the LoRA to learn.
  • Test the trained LoRA in downstream generation workflows before scaling up training volume.

Notes

  • image_data is required.
  • default_caption is optional.
  • steps defaults to 101.
  • Pricing depends only on the selected steps value.
  • LoRAs trained here are intended for Pruna text-to-image LoRA usage.

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 Image Text To Image Trainer API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-image/text-to-image-trainer 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 Trainer below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image_data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip",
    "steps": 101
}
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-trainer" \
  -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/pruna-ai/p-image/text-to-image-trainer";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image_data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip",
        "steps": 101
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "image_data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip",
    "steps": 101
}

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-trainer", 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 Trainer API — Frequently asked questions

What is the P Image Text To Image Trainer API?

P Image Text To Image Trainer is a Pruna Ai model for AI inference, exposed as a REST API on WaveSpeedAI. Pruna AI P-Image Text to Image Trainer is a fast AI model training workflow for customizing text-to-image generation models with user-provided data. Ready-to-use REST inference API for training custom styles, brand-specific visuals, character concepts, product image generation, marketing creatives, and personalized AI image workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the P Image Text To Image Trainer 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/pruna-ai/pruna-ai-p-image-text-to-image-trainer.

How much does P Image Text To Image Trainer cost per run?

P Image Text To Image Trainer starts at $1.80 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 P Image Text To Image Trainer accept?

Key inputs: `default_caption`, `image_data`, `steps`. 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-trainer.

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

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.