kwaivgi/kling-video-o3-std/video-edit

Kling Omni Video O3 Video-Edit (Standard) enables natural-language video edits: remove or replace objects, change backgrounds, add effects, and more. Video duration limited to 10s.

VIDEO-TO-VIDEOVIDEO-EDITNEW
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kwaivgi/kling-video-o3-std/video-edit
Kling Video O3 Std Video-Edit
视频转视频

Kling Omni Video O3 Video-Edit (Standard) enables natural-language video edits: remove or replace objects, change backgrounds, add effects, and more. Video duration limited to 10s.

输入

正在加载参数配置...

输出

空闲
生成的视频将在这里显示
配置参数后点击运行开始生成

每次运行将花费 0.107。$10 可运行约 93 次。

你可以继续:

参数

代码示例

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-video-o3-std/video-edit",
    "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()

安装

安装所需的依赖包。

bash
pip install requests

认证

所有 API 请求需要通过 API Key 进行认证。您可以在 Atlas Cloud 控制台获取 API Key。

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

HTTP 请求头

python
import os

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

切勿在客户端代码或公开仓库中暴露您的 API Key。请使用环境变量或后端代理。

提交请求

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())

提交请求

提交一个异步生成请求。API 返回一个 prediction ID,您可以用它来检查状态和获取结果。

POST/api/v1/model/generateVideo

请求体

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/video-edit",
    "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']}")

响应

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

检查状态

轮询 prediction 端点以检查请求的当前状态。

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

轮询示例

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)

状态值

processing请求仍在处理中。
completed生成完成,输出可用。
succeeded生成成功,输出可用。
failed生成失败,请检查 error 字段。

完成响应

{
  "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"
  }
}

上传文件

将文件上传到 Atlas Cloud 存储,获取可在 API 请求中使用的 URL。使用 multipart/form-data 上传。

POST/api/v1/model/uploadMedia

上传示例

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}")

响应

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

Input Schema

以下参数在请求体中被接受。

总计: 0必填: 0可选: 0

暂无可用参数。

请求体示例

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

Output Schema

API 返回包含生成输出 URL 的 prediction 响应。

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

响应示例

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 将 300+ AI 模型直接集成到您的 AI 编程助手中。一条命令安装,即可用自然语言生成图像、视频和与 LLM 对话。

支持的客户端

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ 支持的客户端

安装

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

设置 API Key

从 Atlas Cloud 控制台获取 API Key,并将其设置为环境变量。

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

功能

安装后,您可以在 AI 助手中使用自然语言访问所有 Atlas Cloud 模型。

图像生成使用 Nano Banana 2、Z-Image 等模型生成图像。
视频创作使用 Kling、Vidu、Veo 等模型从文本或图像创建视频。
LLM 对话与 Qwen、DeepSeek 等大语言模型对话。
媒体上传上传本地文件用于图像编辑和图生视频工作流。

MCP Server

Atlas Cloud MCP Server 通过 Model Context Protocol 将您的 IDE 与 300+ AI 模型连接。支持任何兼容 MCP 的客户端。

支持的客户端

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ 支持的客户端

安装

bash
npx -y atlascloud-mcp

配置

将以下配置添加到您的 IDE 的 MCP 设置文件中。

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

可用工具

atlas_generate_image从文本提示生成图像。
atlas_generate_video从文本或图像创建视频。
atlas_chat与大语言模型对话。
atlas_list_models浏览 300+ 可用 AI 模型。
atlas_quick_generate一步式内容创建,自动选择最佳模型。
atlas_upload_media上传本地文件用于 API 工作流。

API Schema

Schema 不可用

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Kling Video O3 Std Video Edit

Kling Video O3 Standard Video Edit enables natural-language video edits: remove or replace objects, swap backgrounds, restyle scenes, change weather/lighting, and apply localized 3-10s transformations with strong temporal consistency. Built for stable production use with a ready-to-use REST API and predictable performance.

Why Choose This?

Prompt-driven editing Describe your edits in plain language — no timeline, no masks, no manual keyframing required.

Reference image support Attach up to 4 reference images to guide the target element, scene, or style in the output.

Audio preservation Keep the original soundtrack intact with the keep_original_sound option.

Scene-level understanding The model recognizes objects, backgrounds, and context within the video to apply accurate, context-aware edits.

Motion-consistent output Edits blend naturally across frames with strong temporal coherence — minimal flicker or ghosting.

Parameters

ParameterRequiredDescription
promptYesText description of the desired edit
videoYesInput video to edit (URL or upload)
imagesNoUp to 4 reference images for element, scene, or style guidance
keep_original_soundNoWhether to keep the original sound from the video (default: enabled)

How to Use

  1. Run — submit and download the edited video.
  2. Set audio preference — toggle keep_original_sound to preserve or remove original audio.
  3. Add reference images (optional) — attach up to 4 images to steer the look of elements or styles.
  4. Write your prompt — describe exactly what should change (e.g., "Change the beer to Cola.").
  5. Upload your video — drag-and-drop, file upload, or paste a public URL.

Best Use Cases

  • Storytelling & Film — Adjust scene details, atmosphere, or objects to refine narrative visuals in post-production.
  • Creative Exploration — Experiment with style changes, scene swaps, and visual concepts on existing footage.
  • E-commerce — Edit product videos to showcase different variants, colors, or settings from a single source clip.
  • Brand & Marketing — Replace or update branded elements across video assets without reshooting.
  • Social Media Campaigns — Quickly swap products, backgrounds, or props in short-form videos.

Pro Tips

  • Ensure video URLs are publicly accessible — a preview thumbnail in the interface confirms the link works.
  • Test edits on shorter clips first, then apply to longer footage once satisfied.
  • Keep keep_original_sound enabled when audio continuity matters for your project.
  • Reference images work best when they clearly represent the target element or style.
  • Use clear, specific prompts describing exactly what should change for best results.

Notes

  • If using a URL, make sure it is publicly accessible.
  • Billed duration is clamped between 3 and 10 seconds regardless of actual video length.
  • Both prompt and video are required fields.
  • Kling Video O3 Std Text-to-Video — Generate videos from text prompts at Standard pricing.
  • Kling Video O3 Std Image-to-Video — Animate a single image into video at Standard pricing.
  • Kling Video O3 Pro Video Edit — Maximum quality video editing with O3 Pro tier.

300+ 模型,即刻开启,

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