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pruna-ai/

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.

image-to-video
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$0.02cho mỗi lần chạy·~50 / $1

Tiếp theo:

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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.

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README

Pruna AI P-Video Image-to-Video

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.

Why Choose This?

  • 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.

Parameters

ParameterRequiredDescription
imageYesReference image used as the starting visual input for video generation.
promptYesText description of the desired motion, scene progression, camera movement, and style.
durationNoVideo duration in seconds.
resolutionNoOutput resolution: 720p or 1080p.
seedNoRandom seed for reproducibility. Use the same seed to get more consistent results.
save_audioNoWhether to save the generated video with audio.

How to Use

  1. Upload your image — provide the reference image you want to animate.
  2. Write your prompt — describe the motion, subject behavior, camera movement, and mood you want.
  3. Set duration — choose how long the video should be.
  4. Choose resolution — use 720p for lower cost or 1080p for higher quality.
  5. Set a seed (optional) — use a fixed seed for more reproducible generations.
  6. Enable audio saving (optional) — turn on save_audio if you want the saved result to include audio.
  7. Submit — run the model and download the generated video.

Example Prompt

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

Pricing is based on duration and resolution.

ResolutionPer Second
720p$0.02
1080p$0.04

Example Costs

Resolution5s10s15s
720p$0.10$0.20$0.30
1080p$0.20$0.40$0.60

Billing Rules

  • 720p costs $0.02 per second
  • 1080p costs the 720p rate
  • Pricing scales linearly with duration
  • seed and save_audio do not affect pricing

Best Use Cases

  • Character scene animation — Turn still character images into short cinematic moments with subtle motion.
  • Commercial image animation — Bring product, fashion, or editorial stills to life with camera and subject movement.
  • Concept visualization — Convert a keyframe or story image into a motion preview for pitching and ideation.
  • Mood-driven storytelling — Use prompts to shape atmosphere, pacing, and emotional tone from a single image.
  • Social media video — Generate short clips from still images for promotional or narrative content.
  • Creative prototyping — Explore multiple motion directions from the same base image with prompt and seed control.

Pro Tips

  • Use a clean, high-quality reference image for better stability and subject preservation.
  • Be specific in your prompt about motion, camera behavior, and what should remain unchanged.
  • Mention preservation explicitly when identity, outfit, background, or composition must stay consistent.
  • Start with shorter durations to validate motion before generating longer clips.
  • Use 720p for quick testing, then switch to 1080p for higher-quality final outputs.
  • Keep the same seed when you want to iterate on a concept with more consistent results.

Notes

  • Both image and prompt are required.
  • Pricing depends on 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.

Related Models

Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp. Giá trong tài liệu chỉ để tham khảo và có thể đã lỗi thời. Nút Generate hiển thị giá ước tính; phí cuối cùng của tác vụ sẽ được áp dụng.

P Video Image To Video API — Quick start

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.

HTTP example
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
done
Node.js example
const 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));
}
Python example
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 API — Frequently asked questions

What is the P Video Image To Video API?

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.

How do I call the P Video Image To Video API?

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.

How much does P Video Image To Video cost per run?

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.

What inputs does P Video Image To Video accept?

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.

How long does P Video Image To Video take to generate?

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.

Can I use P Video Image To Video outputs commercially?

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.