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Vidu Start-End to Video 2.0 generates smooth transition videos interpolating between given start and end images for natural morphing effects. Ready-to-use REST API, best performance, no coldstarts, affordable pricing.

image-to-video
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$0.3per run·~33 / $10

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ExamplesView all

the iron man transform into the sport car

The car is driving on the road, then changes color

slight body tension, mid-air leap, soft shadow motion, gentle landing

glass flower blossom

The dragon spits fire at the house.

The girl blew bubbles towards the sunset.

Camera pulls back,the beautiful woman shows a brilliant smile at the camera.

The camera pulls back, and the beautiful woman confidently walks on the street.

Zoom out.

Related Models

README

Vidu Start-End-to-Video 2.0 — vidu/start-end-to-video-2.0

Vidu Start-End-to-Video 2.0 generates a short video that transitions from a start image to an end image, guided by a text prompt. You provide the first frame (image) and the final target frame (last_image), then describe the transformation story in the prompt. The model fills in the in-between motion and visual continuity, making it ideal for morphs, product reveals, character transformations, and “before → after” storytelling.## Key capabilities

  • Start-to-end guided video generation (image → last_image)
  • Prompt-driven transformation with controllable motion intensity
  • Designed for smooth visual transitions and narrative continuity
  • Seed control for reproducible variations

Use cases

  • Character transformation and morph sequences (human → creature, outfit swaps, style shifts)
  • Product reveals (closed box → unboxed product, sketch → final render)
  • Before/after transitions (room makeover, color grading shift, weather change)
  • Logo or brand animations (static mark → dynamic finish frame)
  • Short cinematic transitions for ads and social clips

Pricing

OutputPrice per run
Start-End-to-Video (per generation)$0.30

Inputs

  • prompt (required): describe the transition and what happens in-between
  • image (required): start image (first frame)
  • last_image (required): end image (final frame)

Parameters

  • seed: random seed (-1 for random; fixed for reproducible results)

Prompting guide (start → end)

Write prompts that explain how the start becomes the end:

  • Describe the transformation mechanism (morphing, unfolding, dissolving, assembling)
  • Call out key beats (sparks, parts shifting, panels opening, smoke/energy)
  • Mention camera intent (fixed shot, slow push-in) if you need it
  • Keep continuity constraints clear (same framing, stable background, consistent lighting)

Template: Fixed camera. Start as [start]. The subject transforms by [mechanism] into [end]. Smooth transition, coherent motion, consistent lighting, no flicker.

Example prompts

  • A metal suit transforms into a sleek sports car through mechanical folding panels, glowing arc energy, parts assembling smoothly, fixed camera, cinematic lighting, seamless morph from start to end.
  • A sketch drawing gradually fills with color and detail, turning into a polished illustration, gentle dust particles, slow push-in, smooth transition.
  • A winter landscape melts into spring: snow dissolves, grass emerges, flowers bloom, warm sunlight grows stronger, smooth continuous motion.
Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Start End To Video 2.0 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/vidu/start-end-to-video-2.0 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 2.0 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"
}
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-2.0" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/vidu/start-end-to-video-2.0";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "last_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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));
}
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"
}

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-2.0", 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)

Start End To Video 2.0 API — Frequently asked questions

What is the Start End To Video 2.0 API?

Start End To Video 2.0 is a Vidu model for video generation from images, exposed as a REST API on WaveSpeedAI. Vidu Start-End to Video 2.0 generates smooth transition videos interpolating between given start and end images for natural morphing effects. Ready-to-use REST 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 2.0 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-2.0.

How much does Start End To Video 2.0 cost per run?

Start End To Video 2.0 starts at $0.30 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 2.0 accept?

Key inputs: `prompt`, `image`, `seed`, `last_image`. 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-2.0.

How long does Start End To Video 2.0 take to generate?

Median end-to-end generation time on WaveSpeedAI is around 77 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 2.0 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.