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Vidu Start End to Video Q1

vidu /

Vidu Q1 Start-End To Video turns specified start and end images into smooth image-to-video transitions for morphs and scene fades. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-video
Đầu vào

Chờ

$0.4cho mỗi lần chạy·~25 / $10

Tiếp theo:

Ví dụXem tất cả

The robots show their light saber and fight

The photo shows a time-lapse from morning to night as buildings light up and fireworks begin to go off.

After sowing, the flowers slowly germinate and eventually bloom.

The woman in picture 1 changes her clothes in picture 2.

Autobot transformation

Iron Man puts on his armor.

Narrative: Sherlock leaves his lamplit study and arrives at the outdoor crime scene to investigate. Identity & Wardrobe: preserve the same face, hair, coat, scarf, and props (e.g., magnifying glass). Lighting & Atmosphere: warm tungsten indoors → cool, foggy dawn outdoors; faint breath vapor. Motion path: study (slow push-in) → corridor/street (match cut) → tracking arrival at scene; end on close-up over evidence. Camera: gentle dolly and parallax; no jump cuts; natural coat sway; subtle wind. Color grade: muted Victorian palette; soft film grain; crisp micro-contrast on eyes and hands. Final beat: Sherlock kneels or leans, focused gaze on a small clue near the ground.

The man put on a helmet and started riding a motorcycle

Mô hình liên quan

README

Vidu Q1 — Start-End to Video

Vidu Q1 Start-End to Video generates smooth, coherent motion sequences between a specified start frame and end frame, transforming static images into cinematic 5-second transitions. Built on the Vidu Q-series architecture, it delivers high-quality motion interpolation, making it ideal for professional storytelling, editing, and scene development.

Key Features

  • Bi-frame Guided Synthesis Generates realistic motion by interpreting both start and end frames, ensuring a seamless visual flow.

  • Strong Narrative Continuity Preserves scene logic and emotional tone across frames, maintaining coherent storytelling through motion.

  • Object- and Human-Aware Motion Handles complex transitions involving people, objects, and environments with spatial consistency and natural dynamics.

  • Adaptive Camera Behavior Simulates camera pans, zooms, or layout changes to achieve cinematic motion depth.

  • High-Fidelity Quality (720p) Provides production-ready visuals with accurate textures, lighting, and temporal consistency.

Use Cases

  • Storyboarding and concept animation
  • Scene interpolation for long-form content or cinematic projects
  • Instructional or educational visual transitions
  • Film previsualization and creative prototyping

Pricing

ResolutionDurationCost per Clip
720p5s$0.40

How to Use

  1. Upload your start frame and end frame (JPEG/PNG).
  2. Optionally include a prompt describing the desired motion or transition style.
  3. Adjust movement_amplitude (auto, small, medium, large).
  4. Click Run to generate your cinematic transition video and download it.
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

Start End To Video Q1 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/vidu/start-end-to-video-q1 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 Start End To Video Q1 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",
    "last_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "movement_amplitude": "auto",
    "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/vidu/start-end-to-video-q1" \
  -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/vidu/start-end-to-video-q1";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "last_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "movement_amplitude": "auto",
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
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",
    "last_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "movement_amplitude": "auto",
    "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/vidu/start-end-to-video-q1", 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)

Start End To Video Q1 API — Frequently asked questions

What is the Start End To Video Q1 API?

Start End To Video Q1 is a Vidu model for video generation from images, exposed as a REST API on WaveSpeedAI. Vidu Q1 Start-End To Video turns specified start and end images into smooth image-to-video transitions for morphs and scene fades. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Start End To Video Q1 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/vidu/vidu-start-end-to-video-q1.

How much does Start End To Video Q1 cost per run?

Start End To Video Q1 starts at $0.40 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 Start End To Video Q1 accept?

Key inputs: `prompt`, `image`, `seed`, `last_image`, `movement_amplitude`. 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/vidu/vidu-start-end-to-video-q1.

How long does Start End To Video Q1 take to generate?

Median end-to-end generation time on WaveSpeedAI is around 137 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 Start End To Video Q1 outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Vidu). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.