vidu/start-end-to-video-2.0

圖生影片

Vidu Start-End-to-Video 2.0 API by Vidu

vidu/start-end-to-video-2.0

Start-end-to-video-2.0

Open and Advanced Large-Scale Video Generative Models.

Vidu Start-End-to-Video 2.0 由 ShengShu Technology 開發。Atlas Cloud（由 Atlas Cloud AI LLC 營運）僅提供接取服務，並不擁有該模型。所有商標均歸其各自所有者所有。

輸入

提示詞 *

圖片 *(0/2)

可拖曳檔案至此，或點擊上傳

MAX:2

時長

運動幅度

種子

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 $0.075。$10 可執行約 133 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": "vidu/start-end-to-video-2.0",  # Required. model name
    "seed": 0,  # The random seed to use for the generation
    "images": [
        "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
        "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
    ],  # Required. Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. default: ["https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg","https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"]
    "prompt": "the iron man transform into the sport car ",  # Required. Text prompt: A textual description for video generation, with a maximum length of 1500 characters
    "duration": 4,  # The duration of the generated media in seconds. (min: 4). options: 4 | 8
    "movement_amplitude": "auto",  # The movement amplitude of objects in the frame. options: auto | small | medium | large
}

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

安裝所需的相依套件。

pip install requests

驗證

所有 API 請求都需要透過 API Key 進行認證。您可以在 Atlas Cloud 控制台取得 API Key。

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

HTTP 標頭

import os

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

保護好您的 API Key

切勿在客戶端程式碼或公開儲存庫中暴露您的 API Key。請使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

提交一個非同步生成請求。API 會傳回一個 prediction ID，您可以用它來檢查狀態與取得結果。

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": "vidu/start-end-to-video-2.0",
    "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['data']['id']}")
print(f"Status: {result['data']['status']}")

回應

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

檢查狀態

輪詢 prediction 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，輸出可用。

succeeded生成成功，輸出可用。

failed生成失敗，請檢查 error 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

將檔案上傳到 Atlas Cloud 儲存空間，取得可在 API 請求中使用的 URL。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
  }
}

Input Schema

以下參數在請求主體中被接受。

總計: 6必填: 3選填: 3

modelstringrequired

model name

Default: "vidu/start-end-to-video-2.0"

seedinteger

The random seed to use for the generation.

Default: 0

imagesarray[string]required

Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. The model will use these provided images to generate the video. For fields that accept images: Only accept 2 images; The pixel density of the start frame and end frame should be similar. The pixel of the start frame divided by the end frame should be between 0.8 and 1.25; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The aspect ratio of the images must be less than 1:4 or 4:1; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string.

Default: ["https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg","https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"]Min items: 2Max items: 2

promptstringrequired

Text prompt: A textual description for video generation, with a maximum length of 1500 characters.

Default: "the iron man transform into the sport car "

durationinteger

The duration of the generated media in seconds.

Default: 4Min: 4

movement_amplitudestring

The movement amplitude of objects in the frame. Defaults to auto, accepted value: auto small medium large.

Default: "auto"

autosmallmediumlarge

範例請求主體

{
  "model": "vidu/start-end-to-video-2.0",
  "seed": 0,
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ],
  "prompt": "the iron man transform into the sport car ",
  "duration": 4,
  "movement_amplitude": "auto"
}

Output Schema

API 傳回包含生成輸出 URL 的 prediction 回應。

idstring

Unique identifier for the prediction, the ID of the prediction to get.

modelstring

Model ID used for the prediction.

statusstring

Status of the task: created, processing, completed, or failed.

outputsarray

Array of URLs to the generated content (empty when status is not completed).

created_atstring

ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).

範例回應

{
  "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 400+ AI 模型直接整合到您的 AI 程式碼助手中。一條命令安裝，即可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

npx skills add AtlasCloudAI/atlas-cloud-skills

設定 API Key

從 Atlas Cloud 控制台取得 API Key，並將其設定為環境變數。

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生影片工作流程。

MCP Server

Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 400+ AI 模型連接。支援任何相容 MCP 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

npx -y atlascloud-mcp

設定

將以下設定新增到您的 IDE 的 MCP 設定檔中。

{
  "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 400+ 可用 AI 模型。

atlas_quick_generate一步式內容創建，自動選擇最佳模型。

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

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "The AtlasCloud API."
  },
  "paths": {
    "/api/v1/model/generateVideo": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/result/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "required": [
          "model",
          "prompt",
          "images"
        ],
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "vidu/start-end-to-video-2.0"
          },
          "seed": {
            "type": "integer",
            "default": 0,
            "description": "The random seed to use for the generation."
          },
          "images": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "default": [
              "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
              "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
            ],
            "maxItems": 2,
            "minItems": 2,
            "description": "Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. The model will use these provided images to generate the video. For fields that accept images: Only accept 2 images; The pixel density of the start frame and end frame should be similar. The pixel of the start frame divided by the end frame should be between 0.8 and 1.25; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The aspect ratio of the images must be less than 1:4 or 4:1; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string."
          },
          "prompt": {
            "type": "string",
            "default": "the iron man transform into the sport car ",
            "description": "Text prompt: A textual description for video generation, with a maximum length of 1500 characters."
          },
          "duration": {
            "enum": [
              4,
              8
            ],
            "step": 4,
            "type": "integer",
            "default": 4,
            "maximun": 8,
            "minimum": 4,
            "description": "The duration of the generated media in seconds."
          },
          "movement_amplitude": {
            "enum": [
              "auto",
              "small",
              "medium",
              "large"
            ],
            "type": "string",
            "default": "auto",
            "description": "The movement amplitude of objects in the frame. Defaults to auto, accepted value: auto small medium large."
          }
        },
        "x-order-properties": [
          "model",
          "images",
          "prompt",
          "duration",
          "movement_amplitude",
          "seed"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "Unique identifier for the prediction, the ID of the prediction to get."
          },
          "urls": {
            "type": "object",
            "description": "Object containing related API endpoints."
          },
          "model": {
            "type": "string",
            "description": "Model ID used for the prediction."
          },
          "status": {
            "type": "string",
            "description": "Status of the task: created, processing, completed, or failed."
          },
          "outputs": {
            "type": "array",
            "items": {
              "type": "string"
            },
            "description": "Array of URLs to the generated content (empty when status is not completed)."
          },
          "created_at": {
            "type": "string",
            "format": "date-time",
            "description": "ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)."
          }
        }
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM 友善的提示詞範本

# vidu/start-end-to-video-2.0

> Open and Advanced Large-Scale Video Generative Models.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateVideo` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `vidu/start-end-to-video-2.0`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"vidu/start-end-to-video-2.0"`

- **`images`** (`array[string]`, _required_):
  Supports input of two images, with the first uploaded image considered as the start frame and the second image as the end frame. The model will use these provided images to generate the video. For fields that accept images: Only accept 2 images; The pixel density of the start frame and end frame should be similar. The pixel of the start frame divided by the end frame should be between 0.8 and 1.25; Images Assets can be provided via URLs or Base64 encode; You must use one of the following codecs: PNG, JPEG, JPG, WebP; The aspect ratio of the images must be less than 1:4 or 4:1; All images are limited to 50MB; The length of the base64 decode must be under 50MB, and it must include an appropriate content type string.
  - Default: `["https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg","https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"]`
  - Min items: 2
  - Max items: 2

- **`prompt`** (`string`, _required_):
  Text prompt: A textual description for video generation, with a maximum length of 1500 characters.
  - Default: `"the iron man transform into the sport car "`

- **`duration`** (`integer`, _optional_):
  The duration of the generated media in seconds.
  - Default: `4`
  - Options: 4, 8
  - Min: 4

- **`movement_amplitude`** (`string`, _optional_):
  The movement amplitude of objects in the frame. Defaults to auto, accepted value: auto small medium large.
  - Default: `"auto"`
  - Options: "auto", "small", "medium", "large"

- **`seed`** (`integer`, _optional_):
  The random seed to use for the generation.
  - Default: `0`



**Required Parameters Example**:

```json
{
  "model": "vidu/start-end-to-video-2.0",
  "prompt": "the iron man transform into the sport car ",
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ]
}
```


**Full Example**:

```json
{
  "model": "vidu/start-end-to-video-2.0",
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ],
  "prompt": "the iron man transform into the sport car ",
  "duration": 4,
  "movement_amplitude": "auto",
  "seed": 0
}
```


### Output Schema

The API returns the following output format:


- **`id`** (`string`, _optional_):
  Unique identifier for the prediction, the ID of the prediction to get.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated content (empty when status is not completed).

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”).



**Example Response**:

```json
{
  "id": "",
  "urls": {},
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "created_at": ""
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateVideo" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "vidu/start-end-to-video-2.0",
  "images": [
    "https://static.atlascloud.ai/media/images/1745494594983907143_liqlhd9u.jpg",
    "https://static.atlascloud.ai/media/images/1745494607637805608_31gIDzwr.jpg"
  ],
  "prompt": "the iron man transform into the sport car ",
  "duration": 4,
  "movement_amplitude": "auto",
  "seed": 0
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/vidu/start-end-to-video-2.0)

暫無可用範例

載入中...

Vidu2.0 Start end to Video creates coherent video by adding motion between the start and end frames, and is an effective tool for scene transitions and storytelling.

Key Features

Bi-frame guided synthesis
Strong narrative continuity
Object-aware and human-aware motion interpolation
Adaptive to camera movement and layout shifts

Use Cases

Storyboarding and concept animation
Scene interpolation in long-form content
Instructional visual sequences
Film previsualization

Accelerated Inference

Our accelerated inference approach leverages advanced optimization technology from WaveSpeedAI. This innovative fusion technique significantly reduces computational overhead and latency, enabling rapid image generation without compromising quality. The entire system is designed to efficiently handle large-scale inference tasks while ensuring that real-time applications achieve an optimal balance between speed and accuracy. For further details, please refer to the blog post.

Vidu Start-End-to-Video 2.0 API by Vidu

輸入

輸出

參數

程式碼範例

安裝

驗證

HTTP 標頭

提交請求

提交請求

請求主體

回應

檢查狀態

輪詢範例

狀態值

完成回應

上傳檔案

上傳範例

回應

Input Schema

範例請求主體

Output Schema

範例回應

Atlas Cloud Skills

支援的客戶端

安裝

設定 API Key

功能

MCP Server

支援的客戶端

安裝

設定

可用工具

API Schema

LLM 友善的提示詞範本

Key Features

Use Cases

Accelerated Inference

探索類似模型

Vidu Q3-Mix Reference to Video

Vidu Q3 Reference to Video

Vidu Q3-Pro Start-end-to-video

Vidu Q3-Turbo Image-to-video

Vidu Q3-Turbo Start-end-to-video

Vidu Q3-Turbo Text-to-video

Vidu Q3-Pro Image-to-video

Vidu Q3-Pro Text-to-video

Vidu Reference-to-Video Q1

Vidu Reference-to-Video 2.0

Image-to-video-2.0

Vidu Q2-Turbo Start-end-to-video

Vidu Q2-Pro Start-end-to-video

Vidu Q2-Pro-Fast Start-end-to-video

Vidu Q2 Reference-to-video

Vidu Q2-Pro Reference-to-video

一個 API，暢享全模態 AI。

輸入

輸出

參數

程式碼範例

安裝

驗證

HTTP 標頭

提交請求

提交請求

請求主體

回應

檢查狀態

輪詢範例

狀態值

完成回應

上傳檔案

上傳範例

回應

Input Schema

範例請求主體

Output Schema

範例回應

Atlas Cloud Skills

支援的客戶端