Paskyroms be pirkimo: paleiديmai virš 2 kreditų reikalingi papildymo.
Naujas čia?
10 nemokami kreditai (0,10 USD), kai užsiregistruojate naudodami Google
Galima naudoti 24 valandų po registracijos, iki 5 paleidimų per dieną ir daugiausia 2 kreditų per paleidimą. Kiti prisijungimo būdai pradedami be kreditų.
02
Examples
Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
Prompt
a dog riding a skateboard
Trukmė: 0:05
Settings
aspect_ratio
16:9
duration
5
Pavyzdiniai užklausos tekstai
Pavyzdžiai iš Railwail katalogo. Jie nebuvo sugeneruoti tiesiogiai šiame puslapyje.
Nature Scene
Trukmė: 0:08
Aerial drone shot slowly rising above a misty old-growth forest at dawn, revealing a winding river cutting through the valley below, golden sunlight breaking through clouds
Cinematic Portrait
Trukmė: 0:05
Close-up of a woman's face as she opens her eyes and looks into the camera, shallow depth of field, soft golden backlight, hair gently moving in the breeze, cinematic 24fps
03
Apie Google Veo 2
TrumpaiNuo 2026 m. rugsėjo 23 d.
Google Veo 2 yra Google DeepMind modelis iš Vaizdo įrašų generavimas kategorijos. Per Railwail Google Veo 2 kainuoja 0,60 USD už vaizdo sekundę. Numatytasis 8 sekundžių paleidimas kainuoja 4,80 USD. Palaikomi aspekto santykiai: 16:9 ir 9:16. Galimos trukmės: 5, 6, 7 ar 8 s. Naujesnė versija: Google Veo 3.1.
Fonas
Apie Google DeepMind
Įkurta 2010 · London, United Kingdom
Google DeepMind is the merged AI research organisation formed in April 2023 when Google Brain and DeepMind (acquired by Google in 2014) were combined into a single unit led by Demis Hassabis (CEO). DeepMind was founded in 2010 by Hassabis, Shane Legg and Mustafa Suleyman, and is famous for AlphaGo, AlphaFold and the Gemini language-model family. On the video side, DeepMind shipped Imagen Video (2022), Lumiere (2024), Veo (May 2024, Google I/O), Veo 2 (December 2024), Veo 3 with native audio (May 2025) and Veo 3.1 (late 2025). Veo is exposed via Vertex AI, the Gemini API, Google Labs (VideoFX, Whisk) and YouTube's Dream Screen.
Latent video diffusion / Diffusion Transformer hybrid with cascaded super-resolution
Veo 2 is a closed text-to-video and image-to-video model that builds on DeepMind's Imagen and Lumiere research. It uses a spatio-temporal latent diffusion architecture with a transformer-based denoiser conditioned on rich text embeddings from Gemini-family encoders and optional image embeddings. Veo 2 generates clips up to 8 seconds at up to 4K resolution (extended versions reach minutes), with strong understanding of camera language ('dolly', 'aerial', '35mm anamorphic') and real-world physics. DeepMind report substantial gains over Veo 1 on motion realism, prompt adherence and detail. The pipeline combines a base diffusion model with cascaded spatial and temporal super-resolution and an upscaler. Veo 2 was trained on a curated YouTube-and-partner video corpus with synthetic captioning at multiple granularities, and post-trained with reward models for prompt adherence and aesthetic quality.
Parametrai
Undisclosed
Galimybės
Text-to-video and image-to-video generation
Up to 8-second clips, up to 4K resolution
Rich cinematographic prompt vocabulary (lenses, lighting, camera moves)
Strong real-world physics and object permanence
Available via Vertex AI, Gemini API, VideoFX and YouTube Dream Screen
SynthID watermarking embedded in every frame
Multilingual prompts via Gemini text encoders
Lower hallucination of artefacts than Veo 1
Best for: high-quality marketing, film previs, broadcast inserts, enterprise creative.
Mokymas ir licencija
Curated mix of licensed video, public web video (including YouTube under Google's terms) and synthetic data, with multi-granularity captioning produced by Gemini vision models. Exact dataset size undisclosed.
Licencija: Proprietary commercial licence via Google Cloud / Vertex AI and the Gemini API; commercial usage permitted under Google's generative AI terms; SynthID watermark required on all output.
Saugumo testai: Subject to Google's responsible AI framework with safety filters, public-figure filters, child-safety classifiers and provenance via SynthID; covered by Google's Responsible AI Progress Reports.
Žinomos ribotybės
No native audio in Veo 2 (audio first appears in Veo 3)
8-second clip limit per base generation
Closed access with gated waitlist on some surfaces
Strict moderation on people, brands and political content
Limited control over exact frame composition without seed images
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google-veo-2",
"prompt": "A slow drone shot over a misty pine forest at sunrise"
}'
# Response: {"job_id": "...", "status": "queued", ...}
# Poll until status is completed, failed or cancelled:
curl https://railwail.com/api/v1/jobs/JOB_ID \
-H "Authorization: Bearer $RAILWAIL_API_KEY"
import os
import time
import requests
API = "https://railwail.com/api/v1"
headers = {"Authorization": f"Bearer {os.environ['RAILWAIL_API_KEY']}"}
job = requests.post(
f"{API}/videos/generations",
headers=headers,
json={
"model": "google-veo-2",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
},
).json()
while True:
status = requests.get(f"{API}/jobs/{job['job_id']}", headers=headers).json()
if status["status"] in ("completed", "failed", "cancelled"):
break
time.sleep(5)
print(status["status"], status.get("output_url"))
const API = "https://railwail.com/api/v1";
const headers = {
Authorization: `Bearer ${process.env.RAILWAIL_API_KEY}`,
"Content-Type": "application/json",
};
const job = await fetch(`${API}/videos/generations`, {
method: "POST",
headers,
body: JSON.stringify({
model: "google-veo-2",
prompt: "A slow drone shot over a misty pine forest at sunrise"
}),
}).then((r) => r.json());
let status;
do {
await new Promise((r) => setTimeout(r, 5000));
status = await fetch(`${API}/jobs/${job.job_id}`, { headers }).then((r) => r.json());
} while (!["completed", "failed", "cancelled"].includes(status.status));
console.log(status.status, status.output_url);
Proprietary commercial licence via Google Cloud / Vertex AI and the Gemini API; commercial usage permitted under Google's generative AI terms; SynthID watermark required on all output.
Katalogo įrašas atnaujintas
2026 m. rugsėjo 23 d.
Įvesties parametrai
Įvestys ir nustatymai iš modelio įvesties schemos. API sekcijoje pateiktas pavyzdys rodo, kuriuos iš jų priima API.
Google Veo 2 yra Google DeepMind modelis iš Vaizdo įrašų generavimas kategorijos. Railwail platformoje galite jį iškviesti naudodami API raktą per Railwail API.
Kiek kainuoja Google Veo 2 Railwail?
Per Railwail Google Veo 2 kainuoja 0,60 USD už vaizdo sekundę. Numatytasis 8 sekundžių paleidimas kainuoja 4,80 USD. Kaina žinoma prieš pradedant vykdymą. Naudojimas apmokamas iš išanksto nupirktų kreditų; 1 kreditas = 0,01 USD.
Kokius nustatymus palaiko Google Veo 2?
Pagal jos įvesties schemą, Google Veo 2 žino šiuos parametrus: prompt, duration (5, 6, 7 ar 8), start_image ir aspect_ratio (16:9 ar 9:16).
Koks yra Google Veo 2 greitis?
Google Veo 2 Railwail platformoje dar nėra pakankamai išmatuotų vykdymų, kad būtų galima nurodyti vykdymo laiką. Jis priklauso nuo įvesties, nustatymų ir teikėjo apkrovos.
Ar Google Veo 2 geriau nei Google Veo 3.1?
Tai priklauso nuo užduoties. Google Veo 2 (Google DeepMind) ir Google Veo 3.1 (Google DeepMind) yra abu modeliai iš Vaizdo įrašų generavimas kategorijos. Palyginimo puslapyje jų kainos ir specifikacijos rodomos viena šalia kitos.
Sukurkite Railwail API raktą ir siųskite savo užklausą su modelio ID google-veo-2. Kodo pavyzdžiai curl, Python ir JavaScript yra šio puslapio API skyriuje.
Kuaishou's Kling v2.1, generating 5 and 10 second videos at 720p or 1080p from text or an image. Known for cinematic camera work and realistic physical motion, available on Replicate via the official KwaiVGI account.
