kwaivgi/kling-v2.6-pro/avatar

圖生影片

PRO

Kling v2.6 Pro Avatar API by Kuaishou

kwaivgi/kling-v2.6-pro/avatar

Avatar

Kling V2 AI Avatar Pro generates high-quality AI avatar videos with clean detail, stable motion, and strong identity consistency—ideal for profiles, intros, and social content.

輸入

正在載入參數設定...

輸出

閒置

生成的影片將在這裡顯示

設定參數後點擊執行開始生成

每次執行將花費 $0.095。$10 可執行約 105 次。

你可以繼續：

Seedance 2.0 Kling v3 Vidu Wan2.7

參數

程式碼範例
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-v2.6-pro/avatar",
    "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()

安裝

為您的程式語言安裝所需的套件。

bash

pip install requests

驗證

所有 API 請求都需要透過 API 金鑰進行驗證。您可以從 Atlas Cloud 儀表板取得 API 金鑰。

bash

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

HTTP 標頭

python

import os

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

請妥善保管您的 API 金鑰

切勿在客戶端程式碼或公開儲存庫中暴露您的 API 金鑰。請改用環境變數或後端代理。

提交請求

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())

提交請求

提交非同步生成請求。API 會傳回一個預測 ID，您可以用它來檢查狀態並取得結果。

POST/api/v1/model/generateVideo

請求主體

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-v2.6-pro/avatar",
    "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']}")

回應

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

檢查狀態

輪詢預測端點以檢查請求的當前狀態。

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

輪詢範例

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)

狀態值

processing請求仍在處理中。

completed生成完成。輸出已可取得。

succeeded生成成功。輸出已可取得。

failed生成失敗。請檢查錯誤欄位。

完成回應

{
  "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"
  }
}

上傳檔案

上傳檔案至 Atlas Cloud 儲存空間並取得 URL，可用於您的 API 請求。使用 multipart/form-data 上傳。

POST/api/v1/model/uploadMedia

上傳範例

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}")

回應

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

輸入 Schema

以下參數可在請求主體中使用。

總計: 0必填: 0選填: 0

無可用參數。

範例請求主體

json

{
  "model": "kwaivgi/kling-v2.6-pro/avatar"
}

輸出 Schema

API 傳回包含生成輸出 URL 的預測回應。

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

範例回應

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 將 300 多個 AI 模型直接整合至您的 AI 程式碼助手。一鍵安裝，即可使用自然語言生成圖片、影片，以及與 LLM 對話。

支援的客戶端

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ 支援的客戶端

安裝

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API 金鑰

從 Atlas Cloud 儀表板取得 API 金鑰，並設為環境變數。

bash

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

功能

安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。

圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。

影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。

LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。

媒體上傳上傳本機檔案，用於圖片編輯和圖片轉影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 300 多個 AI 模型連接。支援任何 MCP 相容的客戶端。

支援的客戶端

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ 支援的客戶端

安裝

bash

npx -y atlascloud-mcp

設定

將以下設定新增至您 IDE 的 MCP 設定檔中。

json

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

可用工具

atlas_generate_image根據文字提示生成圖片。

atlas_generate_video從文字或圖片創建影片。

atlas_chat與大型語言模型對話。

atlas_list_models瀏覽 300 多個可用的 AI 模型。

atlas_quick_generate一步完成內容創建，自動選擇模型。

atlas_upload_media上傳本機檔案用於 API 工作流程。

了解更多

github.com/AtlasCloudAI/mcp-server

API Schema

Schema 不可用

請登入以檢視請求歷史

您需要登入才能存取模型請求歷史記錄。

登入

Kling-v2-ai-avatar-pro — Talking Avatar from Image + Audio

kling-v2-ai-avatar-pro turns a single portrait into a lip-synced talking-head video driven by your own audio. Upload a clear face image, provide a narration or dialogue track, and the model generates a vertical HD avatar clip that speaks and moves naturally on camera.

🌟 Highlights

Audio-driven performance – Uses your uploaded audio as-is (no TTS), keeping timing, pauses and emotion.
Photo-real talking avatar – Animates the face, eyes and head while preserving the identity from the reference image.
One-shot setup – Just an image + audio; no need for video capture or motion recording.
Portrait-ready output – Produces social-ready vertical video that fits Reels, TikTok, Shorts and story formats.
Prompt-guided styling (optional) – Use prompt to hint at camera feel or mood (e.g. “soft studio lighting, subtle head movement, gentle smile”).

🔧 Parameters

audio* – Required. The voice track that drives lip-sync and timing (URL or upload).
image* – Required. A clear, front-facing portrait of the person to animate.
prompt – Optional text describing style, expression or camera feel. If omitted, the model uses a neutral talking-head style.

Tip: Use a well-lit, unobstructed face (no heavy motion blur, minimal occlusion) for best identity preservation.

🚀 How to Use

Upload audio

Clean mono/stereo track, with minimal background noise. Make sure the final edited length matches what you want in the video. 2. Upload image

Front or 3/4 view, eyes visible, face not cropped. The avatar’s identity and pose come from this image. 3. (Optional) Add a prompt

Guide expression or style, e.g.:

“confident presenter in a tech promo, subtle head nods” “friendly customer service tone, warm expression” 4. Run the model

The video length is automatically derived from the audio duration. Download the generated talking-head clip and drop it into your editor or directly onto social platforms.

💰 Pricing

Billing is based on audio duration, with a minimum of 5 seconds.

Audio length (s)	Billed seconds	Price (USD)
0–5	5	0.56
10	10	1.12
20	20	2.24
30	30	3.36
60	60	6.72

Any clip shorter than 5 seconds is still billed as 5 seconds.

🧠 Tips for Best Results

Edit your audio first – Remove mistakes, long silences and background noise before upload.
Match tone to use case – Calm, even delivery for corporate avatars; more expressive reads for ads or UGC.
Keep framing consistent – Use images with similar head size and framing across a campaign for a unified look.
Test a few portraits – Small changes in the reference image (lighting, angle) can noticeably change the avatar's feel.