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.
Idle

$1.8per run
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.
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.
| Parameter | Required | Description |
|---|---|---|
| image_data | Yes | Training image data used to create the LoRA. |
| default_caption | No | Optional default caption applied across the training workflow for more consistent conditioning. |
| steps | No | Number of training steps. Higher values generally increase training time and cost. Default: 101. |
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 is based on the selected steps value.
| Steps | Cost |
|---|---|
| 100 | $0.18 |
| 101 | $0.1818 |
| 250 | $0.45 |
| 500 | $0.90 |
| 1000 | $1.80 |
| 2000 | $3.60 |
stepssteps values increase total training cost proportionallydefault_caption does not affect pricingdefault_caption when your dataset shares a common concept, subject, or style cue.steps gradually if the initial LoRA is too weak or underfit.image_data is required.default_caption is optional.steps defaults to 101.steps value.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; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for P Image Text To Image Trainer below.
# Submit the prediction
curl -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 '{
"steps": 101
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("pruna-ai/p-image/text-to-image-trainer", {
"steps": 101
});
console.log(result.outputs[0]); // → URL of the generated output# pip install wavespeed
import wavespeed
output = wavespeed.run(
"pruna-ai/p-image/text-to-image-trainer",
{
"steps": 101
}
)
print(output["outputs"][0]) # → URL of the generated outputP 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.
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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/pruna-ai/pruna-ai-p-image-text-to-image-trainer.
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.
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.
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.