Google Veo 3.1 Fast is an Image-to-Video model with native 1080p output for high-detail videos from images and fast performance. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Boşta
$1.2çalıştırma başına
A cheerful gorilla holding a camera talks to the audience in vlog style, standing in a jungle with cinematic lighting. The gorilla waves and says with excitement: "Yo! I just made this with Veo 3.1 on WaveSpeedAI! It even gave me a voice! Let’s gooo!" Background: upbeat jungle music, camera shake, 4K cinematic realism.
A street reporter holds a mic and speaks to camera with excitement. Dialogue: "Breaking news! Veo 3.1 just dropped on WaveSpeedAI. It can talk, act, and think—on video!" Fast zooms, background city traffic, upbeat tone.
A driver films a quick car vlog at a rest stop, golden-hour sky. Dialogue: "Pulled over just to test Veo 3.1 on WaveSpeedAI — it even got the car sounds right!" Wind noise, moving reflections, realistic tone.
Two people sitting on a park bench, phone on tripod. Dialogue (Person 1): "You actually made this with AI?" Person 2: "Yeah — Veo 3.1 on WaveSpeedAI." Person 1: "Wild, right?" Natural daylight, birds chirping, authentic moment.
Flower blossom.
Veo 3 I2V Fast is the high-speed, cost-optimized variant of Google DeepMind's Veo 3 image-to-video model. It transforms static images into cinematic 1080p videos with smooth, realistic motion and natural lighting — all while delivering results up to 30% faster than the standard version. Perfect for creators who need rapid, high-quality motion generation for social content, concept visualization, and creative storytelling.
From Image to Motion Transform a single image into a natural, dynamic video sequence while preserving its original composition and style.
Cinematic Realism Produces high-fidelity motion with natural lighting, accurate perspective, and fluid camera transitions.
Native Audio Generation Automatically generates synchronized sound—including ambient noise, effects, and light music—perfectly aligned with the visuals.
Dialogue & Lip-Sync Enables speaking characters or realistic expressions, ideal for storytelling, marketing, and short-form content.
Consistent Subject & Style Retains the identity, color tone, and visual integrity of your input image throughout the motion sequence.
Every run needs $1.2 (both 720p and 1080p)
Without audio needs $0.8
✅ Commercial use allowed
Upload an Image Choose a clear, high-quality still image—this defines the subject, framing, and overall style.
Write a Prompt Describe the desired motion, mood, and camera movement.
Example: “Slow cinematic zoom out as wind moves through the trees and sunlight flickers across the leaves.”
Adjust Settings Select the video duration (up to 8 seconds) and output resolution (up to 1080p).
Generate the Video Submit your prompt and image—Veo 3 I2V automatically creates motion, lighting, and audio.
Preview & Download Review the result, refine the prompt if needed, and download the final MP4.
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/google/veo3.1-fast/image-to-video with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Veo3.1 Fast Image To Video below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "16:9",
"duration": 8,
"resolution": "1080p",
"generate_audio": true
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/google/veo3.1-fast/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/veo3.1-fast/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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"aspect_ratio": "16:9",
"duration": 8,
"resolution": "1080p",
"generate_audio": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${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",
"aspect_ratio": "16:9",
"duration": 8,
"resolution": "1080p",
"generate_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/google/veo3.1-fast/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)Veo3.1 Fast Image To Video is a Google model for video generation from images, exposed as a REST API on WaveSpeedAI. Google Veo 3.1 Fast is an Image-to-Video model with native 1080p output for high-detail videos from images and fast performance. 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-veo3.1-fast-image-to-video.
Veo3.1 Fast Image To Video starts at $1.20 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-veo3.1-fast-image-to-video.
Median end-to-end generation time on WaveSpeedAI is around 116 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.