Veo3.1 Text-to-video
teks-ke-video

Veo3.1 Text-to-Video API by Google

google/veo3.1/text-to-video
Text-to-video

Generate high-fidelity videos from text prompts with Google’s most advanced generative video model. Veo 3.1 delivers cinematic quality, dynamic camera motion, and lifelike detail for storytelling and creative production.

INPUT

Memuat konfigurasi parameter...

OUTPUT

Menunggu
Video yang dihasilkan akan muncul di sini
Konfigurasikan pengaturan Anda dan klik Jalankan untuk memulai

Permintaan Anda akan dikenakan biaya $0.2 per eksekusi. Dengan $10 Anda dapat menjalankan model ini sekitar 50 kali.

Berikut yang dapat Anda lakukan selanjutnya:

Parameter

Contoh kode

import requests
import time

# Step 1: Start video generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "google/veo3.1/text-to-video",
    "prompt": "A beautiful sunset over the ocean with gentle waves",
    "width": 512,
    "height": 512,
    "duration": 3,
    "fps": 24,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] in ["completed", "succeeded"]:
            print("Generated video:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

video_url = check_status()

Instalasi

Instal paket yang diperlukan untuk bahasa pemrograman Anda.

bash
pip install requests

Autentikasi

Semua permintaan API memerlukan autentikasi melalui API key. Anda bisa mendapatkan API key dari dasbor Atlas Cloud.

bash
export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

python
import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}
Jaga keamanan API key Anda

Jangan pernah mengekspos API key Anda di kode sisi klien atau repositori publik. Gunakan variabel lingkungan atau proxy backend sebagai gantinya.

Kirim permintaan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Kirim Permintaan

Kirim permintaan pembuatan asinkron. API mengembalikan prediction ID yang dapat Anda gunakan untuk memeriksa status dan mengambil hasil.

POST/api/v1/model/generateVideo

Isi Permintaan

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateVideo"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "google/veo3.1/text-to-video",
    "input": {
        "prompt": "A beautiful sunset over the ocean with gentle waves"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Respons

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Periksa Status

Polling prediction endpoint untuk memeriksa status permintaan Anda saat ini.

GET/api/v1/model/prediction/{prediction_id}

Contoh Polling

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Nilai Status

processingPermintaan masih diproses.
completedPembuatan selesai. Output tersedia.
succeededPembuatan berhasil. Output tersedia.
failedPembuatan gagal. Periksa field error.

Respons Selesai

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.mp4"
    ],
    "metrics": {
      "predict_time": 45.2
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Unggah File

Unggah file ke penyimpanan Atlas Cloud dan dapatkan URL yang dapat Anda gunakan dalam permintaan API Anda. Gunakan multipart/form-data untuk mengunggah.

POST/api/v1/model/uploadMedia

Contoh Unggah

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Respons

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

Parameter berikut diterima di isi permintaan.

Total: 0Wajib: 0Opsional: 0

Tidak ada parameter yang tersedia.

Contoh Isi Permintaan

json
{
  "model": "google/veo3.1/text-to-video"
}

Output Schema

API mengembalikan respons prediction dengan URL output yang dihasilkan.

idstringrequired
Unique identifier for the prediction.
statusstringrequired
Current status of the prediction.
processingcompletedsucceededfailed
modelstringrequired
The model used for generation.
outputsarray[string]
Array of output URLs. Available when status is "completed".
errorstring
Error message if status is "failed".
metricsobject
Performance metrics.
predict_timenumber
Time taken for video generation in seconds.
created_atstringrequired
ISO 8601 timestamp when the prediction was created.
Format: date-time
completed_atstring
ISO 8601 timestamp when the prediction was completed.
Format: date-time

Contoh Respons

json
{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.mp4"
  ],
  "metrics": {
    "predict_time": 45.2
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills mengintegrasikan 300+ model AI langsung ke asisten pengkodean AI Anda. Satu perintah untuk menginstal, lalu gunakan bahasa alami untuk menghasilkan gambar, video, dan mengobrol dengan LLM.

Klien yang Didukung

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40+ klien yang didukung

Instalasi

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

Atur API Key

Dapatkan API key dari dasbor Atlas Cloud dan atur sebagai variabel lingkungan.

bash
export ATLASCLOUD_API_KEY="your-api-key-here"

Kemampuan

Setelah diinstal, Anda dapat menggunakan bahasa alami di asisten AI Anda untuk mengakses semua model Atlas Cloud.

Pembuatan GambarBuat gambar dengan model seperti Nano Banana 2, Z-Image, dan lainnya.
Pembuatan VideoBuat video dari teks atau gambar dengan Kling, Vidu, Veo, dll.
Obrolan LLMMengobrol dengan Qwen, DeepSeek, dan model bahasa besar lainnya.
Unggah MediaUnggah file lokal untuk pengeditan gambar dan alur kerja gambar-ke-video.

MCP Server

Atlas Cloud MCP Server menghubungkan IDE Anda dengan 300+ model AI melalui Model Context Protocol. Berfungsi dengan klien apa pun yang kompatibel dengan MCP.

Klien yang Didukung

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ klien yang didukung

Instalasi

bash
npx -y atlascloud-mcp

Konfigurasi

Tambahkan konfigurasi berikut ke file pengaturan MCP di IDE Anda.

json
{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Alat yang Tersedia

atlas_generate_imageBuat gambar dari prompt teks.
atlas_generate_videoBuat video dari teks atau gambar.
atlas_chatMengobrol dengan model bahasa besar.
atlas_list_modelsJelajahi 300+ model AI yang tersedia.
atlas_quick_generatePembuatan konten satu langkah dengan pemilihan model otomatis.
atlas_upload_mediaUnggah file lokal untuk alur kerja API.

Schema API

Schema tidak tersedia

Silakan masuk untuk melihat riwayat permintaan

Anda perlu masuk untuk mengakses riwayat permintaan model Anda.

Masuk

Google Veo 3.1 — Text-to-Video (T2V) Model

Veo 3.1 T2V is the latest text-to-video model from Google DeepMind, designed to bring cinematic storytelling to life through text. It generates high-fidelity 1080p videos with synchronized, context-aware audio, realistic motion, and narrative consistency — making it one of the most advanced generative video systems ever released.


Why it stands out

  • Cinematic Realism

    Produces natural lighting, smooth camera transitions, and accurate perspective for film-like motion.

  • Native Audio Generation

    Generates synchronized ambient sound, dialogue, and music directly aligned with the visuals.

  • Dialogue & Lip-Sync

    Supports speaking characters and realistic facial expressions — perfect for storytelling, marketing, or short-form content.

  • Subject Consistency (R2V)

    Maintains a character’s or object’s identity across frames using 1–3 reference images.

  • Video Interpolation

    Seamlessly animates transitions between two given frames — ideal for smooth start-to-end storytelling.

  • Flexible Output

    Supports both 720p and 1080p, at 24 FPS, duration for 4s, 6s, 8s, and in both 16:9 (landscape) and 9:16 (portrait) formats.


Key Parameters

  • prompt — Describe your scene or story (e.g., “A drone shot flying over Las Vegas, transitioning from day to night with soft jazz in the background”).

  • durationSeconds — Choose video length (4s, 6s, or 8s).

  • resolution — 720p or 1080p.

  • aspectRatio — Landscape (16:9) or Portrait (9:16).


Pricing (Preview Stage)

ModelDescriptionInput TypeOutputPrice
Veo 3.1 (Video + Audio)Generate videos with synchronized soundText / ImageVideo + Audio$0.40 / sec
Veo 3.1 (Video only)Generate high-quality silent videosText / ImageVideo$0.20 / sec

Minimum cost: ~$3.20 per clip (based on 8s @ 1080p).


How to Use

  1. Write a Prompt

    Describe the desired motion, camera style, lighting, and sound.

    Example: “A cinematic sunset over the ocean, waves glimmering as seagulls fly across the horizon.”

  2. Adjust Parameters

    Select duration, resolution (720p/1080p), and aspect ratio.

  3. Generate

    Submit your request — Veo 3.1 will render motion, lighting, and synchronized audio.

  4. Preview & Download

    Review your video, refine your prompt if needed, then download the final MP4.


Pro Tips

  • Keep prompts focused on one main action or subject for better coherence.

  • Use camera verbs like “tracking,” “zoom out,” or “handheld” for cinematic control.

  • Mention lighting and mood cues (e.g., “under soft moonlight,” “golden-hour glow”).

  • Use R2V for character-based storytelling; Interpolation for smooth transitions.

  • Avoid conflicting instructions (e.g., “fast zoom” and “slow motion” together).


Notes & Limitations

  • Generation time: ~2–3 minutes for an 8-second 1080p clip.

  • Frame rate fixed at 24 FPS.

  • Advanced controls (R2V, I2V, Interpolation) are mutually exclusive — only one per generation.

  • If your prompt is blocked, rewrite it and resubmit (safety thresholds may adjust during preview).

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