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kwaivgi/kling-v1.6-multi-i2v-standard
Kling v1.6 Multi i2v Standard
gambar-ke-video

Kling v1.6 Multi i2v Standard API by Kuaishou

kwaivgi/kling-v1.6-multi-i2v-standard
Kling-v1.6-multi-i2v-standard

A cost-efficient option for basic image-to-video generation with balanced speed and detail.

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 $0.048 per eksekusi. Dengan $10 Anda dapat menjalankan model ini sekitar 208 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": "kwaivgi/kling-v1.6-multi-i2v-standard",
    "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": "kwaivgi/kling-v1.6-multi-i2v-standard",
    "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": "kwaivgi/kling-v1.6-multi-i2v-standard"
}

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

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
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

Kling AI 1.6 Key Features

  • Improved prompt adherence
    Delivers more precise and fitting responses to user instructions, especially for motion, camera angles, and sequential movements.

  • Upgraded motion dynamics
    Produces more natural and lifelike movements and facial expressions, making character actions fluid and realistic.

  • Enhanced image-to-video quality
    Offers superior color rendering, detailed visuals, realistic lighting and shadows, and consistent style for refined, high-quality videos.

  • Dual mode approach
    Supports both Standard mode for quick video creation and Professional mode for advanced customization and higher-quality production.


Improved Prompt Adherence

Kling 1.6 understands user prompts more intelligently, ensuring dynamic and consistent results aligned with expectations.

Sample:
Original image: girl riding broom
Prompt: Create an anime-style scene of a girl riding a broom in the sky with a black cat. Zoom in to a close-up of the girl's face as she looks ahead with curiosity.


Upgraded Motion Dynamics

Delivers the most realistic human movements and facial expressions, from jumping and punching to subtle gestures, with fluidity almost indistinguishable from real life.

Sample:
Original image: kung fu master
Prompt: A Kung Fu master throws a punch, and the air behind him swirls into the shape of a dragon.


Enhanced Image-to-Video Quality

Features dynamic color rendering, detailed aesthetics, realistic lighting and shadows, and style consistency, producing visually impactful and polished videos.

Sample:
Original image: mechanical wolf
Prompt: A mechanical wolf rises slowly, spreading its limbs smoothly. The camera moves upward as its blue eyes narrow slightly. Neon lights flicker in the background, creating a mysterious futuristic atmosphere.


Dual Model Support

  • Standard mode: Quick and easy AI video creation.
  • Professional mode: Advanced customization for higher-quality, creative video production.

No Extra Costs

All new features and improvements in Kling 1.6 are available to users at no additional cost.

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