kwaivgi/kling-video-o3-std/text-to-video

Kling Omni Video O3 (Standard) is Kuaishou's advanced unified multi-modal video model with MVL (Multi-modal Visual Language) technology. Generates high-quality videos from text prompts with natural motion and audio generation support.

TEXT-TO-VIDEONEW
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kwaivgi/kling-video-o3-std/text-to-video
Kling Video O3 Std Text-to-Video
text-to-video

Kling Omni Video O3 (Standard) is Kuaishou's advanced unified multi-modal video model with MVL (Multi-modal Visual Language) technology. Generates high-quality videos from text prompts with natural motion and audio generation support.

INPUT

Loading parameter configuration...

OUTPUT

Idle
Your generated videos will appear here
Configure your settings and click Run to get started

Your request will cost 0.071 per run. For $10 you can run this model approximately 140 times.

Here's what you can do next:

Parameters

Code Example

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-video-o3-std/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()

Install

Install the required package for your language.

bash
pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

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}"
}
Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

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())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateVideo

Request Body

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-video-o3-std/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']}")

Response

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

Check Status

Poll the prediction endpoint to check the current status of your request.

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

Polling Example

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)

Status Values

processingThe request is still being processed.
completedGeneration is complete. Outputs are available.
succeededGeneration succeeded. Outputs are available.
failedGeneration failed. Check the error field.

Completed Response

{
  "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"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

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}")

Response

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

Input Schema

The following parameters are accepted in the request body.

Total: 0Required: 0Optional: 0

No parameters available.

Example Request Body

json
{
  "model": "kwaivgi/kling-video-o3-std/text-to-video"
}

Output Schema

The API returns a prediction response with the generated output URLs.

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

Example Response

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 integrates 300+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

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

Install

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

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

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.
Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.
LLM ChatChat with Qwen, DeepSeek, and other large language models.
Media UploadUpload local files for image editing and image-to-video workflows.

MCP Server

Atlas Cloud MCP Server connects your IDE with 300+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ supported clients

Install

bash
npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

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

Available Tools

atlas_generate_imageGenerate images from text prompts.
atlas_generate_videoCreate videos from text or images.
atlas_chatChat with large language models.
atlas_list_modelsBrowse 300+ available AI models.
atlas_quick_generateOne-step content creation with auto model selection.
atlas_upload_mediaUpload local files for API workflows.

API Schema

Schema not available

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Kling Video O3 Standard Text-to-Video

Kling Video O3 Standard is Kuaishou's advanced text-to-video model in the O3 family, delivering high-quality cinematic video from text descriptions. With optional synchronized sound generation, multiple aspect ratios, and flexible duration from 3 to 15 seconds, it offers a strong balance of quality and cost.

Why Choose This?

O3-level quality Advanced visual fidelity and motion realism beyond V3.0 models.

Sound generation Optional synchronized sound effects generated alongside the video.

Flexible duration Generate videos from 3 to 15 seconds — any length you need.

Multiple aspect ratios Support for 16:9, 9:16, and 1:1 to fit any platform.

Prompt Enhancer Built-in tool to automatically improve your video descriptions.

Parameters

ParameterRequiredDescription
promptYesText description of the video scene and motion
aspect_ratioNoOutput ratio: 16:9 (default), 9:16, 1:1
durationNoVideo length: 3-15 seconds (default: 5)
soundNoGenerate synchronized sound (default: disabled)

How to Use

  1. Run — submit and download your video.
  2. Enable sound (optional) — generate synchronized audio with the video.
  3. Set duration — choose any length from 3 to 15 seconds.
  4. Select aspect ratio — match your target platform.
  5. Write your prompt — describe the scene, characters, motion, and style in detail.

Best Use Cases

  • Long-Form Scenes — Up to 15 seconds for extended scene development.
  • Concept Visualization — Bring creative ideas to life from text.
  • Marketing Videos — Produce promotional content with optional sound.
  • Social Media — Create engaging videos for TikTok, Reels, and Stories.
  • Professional Content — High-quality videos at a more accessible price than O3 Pro.

Pro Tips

  • Use O3 Standard for regular production; upgrade to O3 Pro for maximum quality.
  • Use shorter durations (3-5s) for testing, longer (10-15s) for final production.
  • Be specific about camera movements, lighting, and atmosphere for best results.
  • Enable sound for a complete video experience with synchronized audio.
  • Match aspect ratio to your platform: 16:9 for YouTube, 9:16 for TikTok/Reels, 1:1 for Instagram.
  • Use the Prompt Enhancer to refine your descriptions automatically.

Notes

  • Duration supports any value from 3 to 15 seconds.
  • Only prompt is required; other parameters have defaults.
  • Kling V3.0 Pro Text-to-Video — V3.0 Pro quality text-to-video.
  • Kling V3.0 Standard Text-to-Video — V3.0 Standard at lower cost.
  • Kling Video O3 Pro Image-to-Video — O3 Pro quality image-to-video.

Start From 300+ Models,

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