vidu/start-end-to-video-2.0

صورة إلى فيديو

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 من تطوير ShengShu Technology. توفّر Atlas Cloud (التي تُشغّلها Atlas Cloud AI LLC) إمكانية الوصول إليه ولا تملكه. جميع العلامات التجارية ملك لأصحابها المعنيين.

الإدخال

الطلب *

الصور *(0/2)

اسحب الملف وأفلته هنا أو انقر للرفع

MAX:2

المدة

مدى الحركة

البذرة

الإخراج

في انتظار التنفيذ

سيظهر الفيديو المُنشأ هنا

قم بتعيين المعاملات وانقر فوق تشغيل لبدء الإنشاء

كل مرة ستكلف $0.075 مع $10 يمكنك التشغيل حوالي 133 مرة

يمكنك المتابعة بـ:

Seedance 2.0 Kling v3 Vidu Wan2.7

المعلمات

مثال الكود
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()

التثبيت

قم بتثبيت الحزمة المطلوبة للغة البرمجة الخاصة بك.

pip install requests

المصادقة

تتطلب جميع طلبات API المصادقة عبر مفتاح API. يمكنك الحصول على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud.

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

ترويسات HTTP

import os

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

حافظ على أمان مفتاح API الخاص بك

لا تكشف أبدًا مفتاح API الخاص بك في الكود من جانب العميل أو المستودعات العامة. استخدم متغيرات البيئة أو وكيل الخادم الخلفي بدلاً من ذلك.

إرسال طلب

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())

إرسال طلب

أرسل طلب توليد غير متزامن. تُرجع API معرّف التنبؤ الذي يمكنك استخدامه للتحقق من الحالة واسترداد النتيجة.

POST/api/v1/model/generateVideo

نص الطلب

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']}")

الاستجابة

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

التحقق من الحالة

استعلم عن نقطة نهاية التنبؤ للتحقق من الحالة الحالية لطلبك.

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

مثال الاستعلام

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)

قيم الحالة

processingلا يزال الطلب قيد المعالجة.

completedاكتمل التوليد. المخرجات متاحة.

succeededنجح التوليد. المخرجات متاحة.

failedفشل التوليد. تحقق من حقل الخطأ.

استجابة مكتملة

{
  "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"
  }
}

رفع الملفات

ارفع الملفات إلى تخزين Atlas Cloud واحصل على URL يمكنك استخدامه في طلبات API الخاصة بك. استخدم multipart/form-data للرفع.

POST/api/v1/model/uploadMedia

مثال الرفع

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}")

الاستجابة

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

Input Schema

المعاملات التالية مقبولة في نص الطلب.

الإجمالي: 6مطلوب: 3اختياري: 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

مثال على نص الطلب

{
  "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

تُرجع API استجابة تنبؤ تحتوي على عناوين URL للمخرجات المولّدة.

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”).

مثال على الاستجابة

{
  "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

يدمج Atlas Cloud Skills أكثر من 400 نموذج ذكاء اصطناعي مباشرة في مساعد البرمجة بالذكاء الاصطناعي الخاص بك. أمر واحد للتثبيت، ثم استخدم اللغة الطبيعية لتوليد الصور ومقاطع الفيديو والدردشة مع LLM.

العملاء المدعومون

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ العملاء المدعومون

التثبيت

npx skills add AtlasCloudAI/atlas-cloud-skills

إعداد مفتاح API

احصل على مفتاح API الخاص بك من لوحة تحكم Atlas Cloud وعيّنه كمتغير بيئة.

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

الإمكانيات

بمجرد التثبيت، يمكنك استخدام اللغة الطبيعية في مساعد الذكاء الاصطناعي الخاص بك للوصول إلى جميع نماذج Atlas Cloud.

توليد الصورأنشئ صورًا باستخدام نماذج مثل Nano Banana 2 و Z-Image والمزيد.

إنشاء الفيديوأنشئ مقاطع فيديو من نص أو صور باستخدام Kling و Vidu و Veo وغيرها.

دردشة LLMتحدث مع Qwen و DeepSeek ونماذج اللغة الكبيرة الأخرى.

رفع الوسائطارفع الملفات المحلية لتحرير الصور وسير عمل تحويل الصور إلى فيديو.

MCP Server

يربط Atlas Cloud MCP Server بيئة التطوير الخاصة بك بأكثر من 400 نموذج ذكاء اصطناعي عبر Model Context Protocol. يعمل مع أي عميل متوافق مع MCP.

العملاء المدعومون

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ العملاء المدعومون

التثبيت

npx -y atlascloud-mcp

التكوين

أضف التكوين التالي إلى ملف إعدادات MCP في بيئة التطوير الخاصة بك.

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

الأدوات المتاحة

atlas_generate_imageتوليد صور من أوصاف نصية.

atlas_generate_videoإنشاء مقاطع فيديو من نص أو صور.

atlas_chatالدردشة مع نماذج اللغة الكبيرة.

atlas_list_modelsتصفح أكثر من 400 نموذج ذكاء اصطناعي متاح.

atlas_quick_generateإنشاء محتوى بخطوة واحدة مع اختيار تلقائي للنموذج.

atlas_upload_mediaرفع الملفات المحلية لسير عمل API.

اعرف المزيد

github.com/AtlasCloudAI/mcp-server

مخطط API

{
  "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"
      }
    }
  }
}

قالب موجه LLM

# 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)

لا توجد أمثلة

جارٍ التحميل...

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

الإدخال

الإخراج

المعلمات

مثال الكود

التثبيت

المصادقة

ترويسات HTTP

إرسال طلب

إرسال طلب

نص الطلب

الاستجابة

التحقق من الحالة

مثال الاستعلام

قيم الحالة

استجابة مكتملة

رفع الملفات

مثال الرفع

الاستجابة

Input Schema

مثال على نص الطلب

Output Schema

مثال على الاستجابة

Atlas Cloud Skills

العملاء المدعومون

التثبيت

إعداد مفتاح API

الإمكانيات

MCP Server

العملاء المدعومون

التثبيت

التكوين

الأدوات المتاحة

مخطط API

قالب موجه LLM

Key Features

Use Cases

Accelerated Inference

استكشف نماذج مماثلة

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

واجهة برمجية واحدة لكل وسائط الذكاء الاصطناعي.

الإدخال

الإخراج

المعلمات

مثال الكود

التثبيت

المصادقة

ترويسات HTTP

إرسال طلب

إرسال طلب

نص الطلب

الاستجابة

التحقق من الحالة

مثال الاستعلام

قيم الحالة

استجابة مكتملة

رفع الملفات

مثال الرفع

الاستجابة

Input Schema

مثال على نص الطلب

Output Schema

مثال على الاستجابة

Atlas Cloud Skills

العملاء المدعومون