Pruna AI P-Image Edit Trainer is a fast AI model training workflow for customizing image editing models with user-provided data. Ready-to-use REST inference API for training custom edit styles, character-consistent edits, product image updates, brand-specific visuals, marketing assets, and personalized AI image editing workflows with simple integration, no coldstarts, and affordable pricing.
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Pruna AI P-Image Edit Trainer is a fast training workflow for creating custom LoRAs for the Pruna image editing stack. Upload your training image data, choose the number of training steps, optionally provide a default caption, and generate a LoRA for downstream edit workflows such as style transfer, character-consistent edits, product edits, and other prompt-guided image editing tasks.
Fast custom edit LoRA training Train LoRAs specifically for image editing workflows rather than text-to-image generation.
Simple training interface Provide training image data and set training steps without a complex setup process.
Optional caption guidance
Use default_caption to provide consistent text guidance across the training data.
Flexible training depth
Adjust steps to balance speed, cost, and how strongly the LoRA learns your dataset.
Built for the Pruna edit stack Trained outputs are intended for downstream use with Pruna edit LoRA workflows.
Production-ready API Suitable for custom edit styles, character-consistent edits, branded asset pipelines, and repeatable editing workflows.
| Parameter | Required | Description |
|---|---|---|
| image_data | Yes | Training image data used to create the edit LoRA. |
| steps | No | Number of training steps. Higher values generally increase training time and cost. Default: 101. |
| default_caption | No | Optional default caption applied to the training workflow for more consistent edit conditioning. |
Train a custom edit LoRA for scene-to-scene style transfer, then use the resulting weights in Pruna AI P-Image Edit LoRA for guided image editing.
Pricing is based on the selected steps value.
| Steps | Cost |
|---|---|
| 100 | $0.40 |
| 101 | $0.404 |
| 250 | $1.00 |
| 500 | $2.00 |
| 1000 | $4.00 |
| 2000 | $8.00 |
stepssteps values increase total training cost proportionallydefault_caption does not affect pricingdefault_caption when you want consistent conditioning across the dataset.image_data is required.steps defaults to 101.default_caption is optional.steps value.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-image/edit-trainer with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Image Edit Trainer below.
set -euo pipefail
: "${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/edit-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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/pruna-ai/p-image/edit-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));
}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/edit-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 Edit Trainer is a Pruna Ai model for AI inference, exposed as a REST API on WaveSpeedAI. Pruna AI P-Image Edit Trainer is a fast AI model training workflow for customizing image editing models with user-provided data. Ready-to-use REST inference API for training custom edit styles, character-consistent edits, product image updates, brand-specific visuals, marketing assets, and personalized AI image editing 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-edit-trainer.
P Image Edit Trainer starts at $4.00 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-edit-trainer.
Median end-to-end generation time on WaveSpeedAI is around 1143 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
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.