Pruna AI P-Video Image to Video is a fast AI video generation model that transforms input images into high-quality videos. Ready-to-use REST inference API for animating product photos, character art, marketing creatives, social media content, visual storytelling, and image-to-video workflows with simple integration, no coldstarts, and affordable pricing.
Idle
$0.02per run·~50 / $1
Use the input image as the first frame. Rain falls gently on the train station platform, wet ground reflections shimmer under warm lights, and the distant train slowly approaches. One person tightens their grip on the suitcase while the other slowly lowers their eyes toward the unopened letter. The camera slowly pushes in between them, emphasizing the emotional distance. Preserve the same characters, faces, outfits, lighting, background, and composition. Cinematic drama, realistic motion, stable identities, no flicker, no distortion.
Pruna AI P-Video Image-to-Video transforms a single reference image into a generated video clip with prompt-guided motion, controllable duration, resolution options, optional audio saving, and seed-based reproducibility. It is suitable for cinematic shots, character scenes, commercial visuals, concept videos, and other image-driven video generation workflows.
Image-guided video generation Start from a reference image and generate motion while preserving the overall scene, subjects, and visual identity.
Prompt-based motion control Use a text prompt to describe subject movement, camera behavior, emotional tone, and scene progression.
Flexible output settings Control duration and resolution based on quality and budget needs.
Optional audio saving
Enable save_audio when you want the saved output to include audio.
Seed support for reproducibility
Use seed to get more consistent results across repeated runs.
Simple pricing model Cost scales clearly with duration and output resolution.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Reference image used as the starting visual input for video generation. |
| prompt | Yes | Text description of the desired motion, scene progression, camera movement, and style. |
| duration | No | Video duration in seconds. |
| resolution | No | Output resolution: 720p or 1080p. |
| seed | No | Random seed for reproducibility. Use the same seed to get more consistent results. |
| save_audio | No | Whether to save the generated video with audio. |
720p for lower cost or 1080p for higher quality.save_audio if you want the saved result to include audio.Two characters stand on a rainy train platform as a distant train approaches. One person tightens their grip on a suitcase while the other slowly lowers their eyes toward the unopened letter. The camera slowly pushes in between them, emphasizing the emotional distance. Preserve the same characters, faces, outfits, lighting, background, and composition while adding subtle cinematic motion and realistic atmosphere.
Pricing is based on duration and resolution.
| Resolution | Per Second |
|---|---|
| 720p | $0.02 |
| 1080p | $0.04 |
| Resolution | 5s | 10s | 15s |
|---|---|---|---|
| 720p | $0.10 | $0.20 | $0.30 |
| 1080p | $0.20 | $0.40 | $0.60 |
720p costs $0.02 per second1080p costs 2× the 720p ratedurationseed and save_audio do not affect pricing720p for quick testing, then switch to 1080p for higher-quality final outputs.seed when you want to iterate on a concept with more consistent results.image and prompt are required.duration and resolution.save_audio controls whether the saved video includes audio, but does not affect pricing.seed helps with reproducibility but may not guarantee identical results in every case.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/pruna-ai/p-video/image-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for P Video Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"resolution": "720p",
"seed": -1,
"save_audio": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/pruna-ai/p-video/image-to-video" \
-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-video/image-to-video";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"resolution": "720p",
"seed": -1,
"save_audio": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"duration": 5,
"resolution": "720p",
"seed": -1,
"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/image-to-video", 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 Video Image To Video is a Pruna Ai model for video generation from images, exposed as a REST API on WaveSpeedAI. Pruna AI P-Video Image to Video is a fast AI video generation model that transforms input images into high-quality videos. Ready-to-use REST inference API for animating product photos, character art, marketing creatives, social media content, visual storytelling, and image-to-video 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-video-image-to-video.
P Video Image To Video starts at $0.020 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`, `image`, `resolution`, `duration`, `seed`, `save_audio`. 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-image-to-video.
Median end-to-end generation time on WaveSpeedAI is around 25 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.