Google Veo2 Image-to-Video creates high-quality videos with realistic motion, varied styles, and precise camera controls for cinematic results. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle
$2.2per run
Four friends drive through the desert in an old van, windows down, hair blowing in the wind. The camera switches between wide shots of the landscape and close-ups of their faces filled with joy. Warm, dusty tones, road-trip music vibe, lens flares, feeling of freedom.
A young woman stands on a rooftop at sunset, overlooking the sprawling cityscape of Los Angeles. She wears a vintage leather jacket and has headphones around her neck. The camera slowly pushes in as she closes her eyes, feeling the wind in her hair. Soft golden light, cinematic color grading, shallow depth of field, lens flares, 4K film grain.
female traveler explores a colorful street market in Marrakech. She smiles as she interacts with local vendors, vibrant fabrics and spices in the background. Natural daylight, handheld camera movement, subtle film look, realistic ambient sounds, documentary tone.
A slow-motion, low-angle shot of a male basketball player dribbling a ball on an urban outdoor court at sunset. Sweat drips from his face as he prepares for a powerful dunk. The dramatic backlighting creates a strong silhouette. Energetic, highly detailed, commercial aesthetic.
A man with a beard sits quietly by a forest lake at dawn, sipping coffee from a tin mug. Mist rises from the water, birds chirp in the background. Warm tones, minimal movement, shallow focus on his face, natural lighting, peaceful and contemplative mood.
A teenage girl stands outside a high school building in the late afternoon sun, holding a backpack and laughing with her friends. The camera captures her from behind, lens flare streaking through the frame. Autumn leaves fall slowly in the breeze. Soft focus, warm colors, light film grain, nostalgic 90s teen movie feel.
A young woman sits alone at a small café terrace in Paris, writing in a notebook. A croissant and coffee rest on the table. Morning sun filters through the trees onto her face. The camera slowly zooms in from across the street. Natural light, muted pastel tones, minimal background noise, elegant and quiet mood.
A man in a red coat stands perfectly still in the center of a vast salt flat, with only the sky and reflection below him. The camera rises slowly into a top-down view. Stark geometry, high contrast, minimalist color palette, surreal and contemplative, experimental art film tone.
A wide shot of a young couple enjoying a meal on the balcony of their apartment in a futuristic megacity. Below them, streams of flying vehicles zip between towering, bio-luminescent skyscrapers. The camera is static, contrasting the quiet, intimate moment with the bustling city below. Photorealistic, 8K.
A teenager in a graphic hoodie sits in his bedroom filled with colorful designer toys and figurines. He picks up a glossy collectible bearbrick and places it on a rotating display. Bright LED lighting, sharp colors, product-focused slow zoom-ins, trendy music-driven rhythm, urban visual style.
Google Veo2 on WaveSpeedAI empowers you to create high-quality videos from both text prompts and static images, leveraging Google's advanced AI for next-generation content creation.
Veo2 on WaveSpeedAI is designed for seamless text-to-video and image-to-video generation. It understands real-world physics, human movement, and cinematic techniques, making it ideal for creators and developers who want to produce visually stunning, dynamic videos at scale.
To get the best results with Veo2 on WaveSpeedAI, try these strategies:
Close-up, two shot, over-the-shoulderMacro lens, shallow focus, wide-angle lensSci-fi, romantic comedy, action movieZoom shot, dolly shot, tracking shot, pan shotA close-up shot of melting icicles on a frozen rock wall, with cool blue tones and a zoom-in camera movement, capturing the detailed motion of water drips.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/veo2/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 Veo2 Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "16:9",
"duration": 5,
"resolution": "720p",
"enable_prompt_expansion": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/google/veo2/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/google/veo2/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "16:9",
"duration": 5,
"resolution": "720p",
"enable_prompt_expansion": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"aspect_ratio": "16:9",
"duration": 5,
"resolution": "720p",
"enable_prompt_expansion": 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/google/veo2/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)Veo2 Image To Video is a Google model for video generation from images, exposed as a REST API on WaveSpeedAI. Google Veo2 Image-to-Video creates high-quality videos with realistic motion, varied styles, and precise camera controls for cinematic results. 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/google/google-veo2-image-to-video.
Veo2 Image To Video starts at $2.20 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`, `aspect_ratio`, `resolution`, `duration`, `seed`. 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/google/google-veo2-image-to-video.
Median end-to-end generation time on WaveSpeedAI is around 41 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 (Google). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.