Pruna P-Video-Edit is an instruction-based video editing model for modifying subjects, objects, attributes, and environments in existing videos, with optional reference-image guidance and Draft or Full quality modes. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$0.045per run·~22 / $1
Transform the entire video into a bold 1970s retro-futurist luxury world while preserving the man’s identity, facial features, walking motion, timing, and original camera movement. Replace the modern hotel lobby with a glamorous space-age lounge featuring curved white architecture, glossy orange furniture, chrome surfaces, circular doorways, geometric carpets, and large panoramic windows overlooking a stylized futuristic city. Change his dark suit into a cream-colored retro-futurist tailored suit with a burnt-orange shirt, wide collar, polished boots, and subtle metallic accessories. Transform the travel bag into a sleek rounded silver case with vintage space-age styling. Keep his original actions unchanged: walking through the lobby, checking his wristwatch, stopping, and looking toward the elevators. Use a bold orange, cream, teal, and chrome color palette, soft film grain, glossy reflections, symmetrical compositions, warm cinematic lighting, sophisticated 1970s science-fiction fashion editorial aesthetic.
Pruna AI P-Video Edit edits an existing video using natural-language instructions. Upload a source video, describe the changes you want, and optionally add reference images to guide identity, appearance, style, object edits, or scene transformation.
Instruction-based video editing
Edit videos with natural-language prompts instead of building a manual editing workflow.
Flexible visual changes
Modify attributes, transform objects, adjust environments, or restyle a scene while preserving the source video's motion and structure.
Reference-guided editing
Add up to 4 reference images to guide identity, appearance, object design, or visual style.
Draft and Full modes
Use Draft mode for faster, lower-cost previews, or Full mode for higher-quality edits.
Audio preservation
Preserve the source video's audio in the edited result with save_audio.
| Parameter | Required | Description |
|---|---|---|
| video | Yes | Source video to edit. Maximum input length: 15 seconds. |
| prompt | Yes | Natural-language instructions describing the requested video changes. |
| images | No | Optional reference images for guided editing. Supports up to 4 JPG, JPEG, PNG, or WebP images. |
| prompt_upsampling | No | Expand and optimize the editing prompt. Default: true. |
| draft | No | Enable faster, lower-cost Draft mode. Default: false. |
| save_audio | No | Preserve the source video's audio in the result. Default: true. |
| seed | No | Random seed for reproducible results. If omitted, the upstream model chooses a random seed. |
15 seconds.save_audio enabled when the source audio should be preserved.Pricing is based on the source video duration and selected mode.
Billing duration is rounded up to the next whole second, with a minimum billed duration of 1 second and a maximum billed duration of 15 seconds.
| Mode | Price per billed second |
|---|---|
| Full | $0.045 |
| Draft | $0.025 |
| Input Duration | Billed Duration | Full | Draft |
|---|---|---|---|
| 0.5s | 1s | $0.045 | $0.025 |
| 6.13s | 7s | $0.315 | $0.175 |
| 15s | 15s | $0.675 | $0.375 |
images, prompt_upsampling, save_audio, and seed do not add separate charges.
save_audio=true when the original soundtrack, dialogue, or ambience should remain.seed when comparing prompt or reference-image changes.video and prompt are required.15 seconds long.images is optional and supports up to 4 reference images.prompt_upsampling=true, draft=false, and save_audio=true.outputs.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-video/edit with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Video Edit below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"prompt_upsampling": true,
"draft": false,
"save_audio": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/edit" \
-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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(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' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/edit";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"prompt_upsampling": true,
"draft": false,
"save_audio": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
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 = {
"video": "https://interactive-examples.mdn.mozilla.net/media/cc0-videos/flower.mp4",
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"prompt_upsampling": True,
"draft": False,
"save_audio": True
}
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-video/edit", 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 = 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", "deleted"}:
raise RuntimeError(result)
time.sleep(2)P Video Edit is a Pruna Ai model for video editing, exposed as a REST API on WaveSpeedAI. Pruna P-Video-Edit is an instruction-based video editing model for modifying subjects, objects, attributes, and environments in existing videos, with optional reference-image guidance and Draft or Full quality modes. Ready-to-use REST inference API, best performance, no coldstarts, 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-video-edit.
P Video Edit starts at $0.045 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`, `video`, `seed`, `draft`, `prompt_upsampling`. 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-video-edit.
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.