Pruna AI P-Image Edit LORA is a fast AI image editing model that edits and transforms images with LORA-based customization. Ready-to-use REST inference API for text-guided image editing, style changes, character consistency, product image updates, marketing assets, and custom AI editing workflows with simple integration, no coldstarts, and affordable pricing.
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$0.01cho mỗi lần chạy·~100 / $1

Make this scene look like the next scene style.
Pruna AI P-Image Edit LoRA edits one or more input images using a natural-language instruction, with optional LoRA guidance for stronger style or edit control. It is designed for workflows where you want prompt-based image editing together with a LoRA trained specifically for the Pruna p-image-edit-lora pipeline.
LoRA-guided image editing Edit images with natural-language instructions while steering the result with a compatible LoRA.
Multi-image reference support Use one to five input images to guide appearance, structure, composition, or scene transformation.
Edit-specific LoRA control
Apply lora_weights and tune lora_scale for stronger stylistic or transformation control.
Flexible aspect ratio handling
Use match_input_image to follow the first input image by default, or select a preset aspect ratio when needed.
Private LoRA support
Use hf_api_token when accessing a private or gated Hugging Face LoRA repository.
Simple fixed pricing Each run uses a flat per-image price.
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text instruction describing the desired edit. |
| images | Yes | One to five reference images used for the edit. When using multiple images, describe their roles clearly in the prompt. |
| lora_weights | No | Optional Hugging Face LoRA path, such as huggingface.co/PrunaAI/p-image-edit-next-scene-lora/weights.safetensors. The LoRA should be trained for p-image-edit-lora. |
| lora_scale | No | LoRA strength. Default: 0.5. Official range: -1 to 3. |
| hf_api_token | No | Optional Hugging Face token for private or gated LoRA repositories. |
| aspect_ratio | No | Output aspect ratio. Default: match_input_image, which follows the first input image. Other supported values: 1:1, 16:9, 9:16, 4:3, 3:4, 3:2, and 2:3. |
| output_format | No | Output image format: png, jpeg, or webp. |
| seed | No | Random seed. Use -1 for random generation. |
lora_weights if you want LoRA-guided editing.lora_scale to control how strongly the LoRA affects the result.match_input_image to follow the first input image, or select a preset ratio if needed.hf_api_token if your LoRA is private or gated.png, jpeg, or webp.-1 for random output, or a fixed value for more reproducible edits.Make this scene look like the next scene style.
Just $0.01 per generated image.
match_input_image when you want to preserve the framing of the first input image.lora_scale gradually to balance prompt influence and LoRA influence.hf_api_token.seed when you want more consistent edit iterations.prompt and images are required.images supports one to five input images.lora_weights is optional.aspect_ratio defaults to match_input_image, which follows the first input image.seed uses -1 for random generation.turbo=false and disables the safety checker by default in the internal mapping; these are not user-facing controls.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-image/edit-lora with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Image Edit Lora below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"lora_scale": 1,
"aspect_ratio": "match_input_image",
"output_format": "png",
"seed": -1
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-image/edit-lora" \
-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-lora";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"lora_scale": 1,
"aspect_ratio": "match_input_image",
"output_format": "png",
"seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"images": [
"https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
],
"lora_scale": 1,
"aspect_ratio": "match_input_image",
"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/edit-lora", 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 Lora is a Pruna Ai model for AI inference, exposed as a REST API on WaveSpeedAI. Pruna AI P-Image Edit LORA is a fast AI image editing model that edits and transforms images with LORA-based customization. Ready-to-use REST inference API for text-guided image editing, style changes, character consistency, product image updates, marketing assets, and custom AI 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-lora.
P Image Edit Lora starts at $0.010 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`, `images`, `aspect_ratio`, `seed`, `enable_base64_output`, `enable_sync_mode`. 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-lora.
Median end-to-end generation time on WaveSpeedAI is around 4 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.