vidu/start-end-to-video-2.0

image-vers-vidéo

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 est développé par ShengShu Technology. Atlas Cloud (exploité par Atlas Cloud AI LLC) en fournit l’accès mais n’en est pas propriétaire. Toutes les marques appartiennent à leurs détenteurs respectifs.

Entrée

Prompt *

Images *(0/2)

Vous pouvez glisser-déposer le fichier ou cliquer pour télécharger

MAX:2

Durée

Amplitude du mouvement

Graine

Sortie

Inactif

Les vidéos générées apparaîtront ici

Configurez vos paramètres et cliquez sur exécuter pour commencer

Votre requête coûtera $0.075 par exécution. Avec $10, vous pouvez exécuter ce modèle environ 133 fois.

Vous pouvez continuer avec :

Seedance 2.0 Kling v3 Vidu Wan2.7

Paramètres

Exemple de code
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()

Installer

Installez le package requis pour votre langage.

pip install requests

Authentification

Toutes les requêtes API nécessitent une authentification via une clé API. Vous pouvez obtenir votre clé API depuis le tableau de bord Atlas Cloud.

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

En-têtes HTTP

import os

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

Protégez votre clé API

N'exposez jamais votre clé API dans du code côté client ou dans des dépôts publics. Utilisez plutôt des variables d'environnement ou un proxy backend.

Soumettre une requête

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())

Soumettre une requête

Soumettez une requête de génération asynchrone. L'API renvoie un identifiant de prédiction que vous pouvez utiliser pour vérifier le statut et récupérer le résultat.

POST/api/v1/model/generateVideo

Corps de la requête

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']}")

Réponse

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

Vérifier le statut

Interrogez le point de terminaison de prédiction pour vérifier le statut actuel de votre requête.

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

Exemple d'interrogation

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)

Valeurs de statut

processingLa requête est encore en cours de traitement.

completedLa génération est terminée. Les résultats sont disponibles.

succeededLa génération a réussi. Les résultats sont disponibles.

failedLa génération a échoué. Vérifiez le champ d'erreur.

Réponse terminée

{
  "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"
  }
}

Téléverser des fichiers

Téléversez des fichiers vers le stockage Atlas Cloud et obtenez une URL utilisable dans vos requêtes API. Utilisez multipart/form-data pour le téléversement.

POST/api/v1/model/uploadMedia

Exemple de téléversement

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}")

Réponse

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

Schema d'entrée

Les paramètres suivants sont acceptés dans le corps de la requête.

Total: 6Requis: 3Optionnel: 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

Exemple de corps de requête

{
  "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"
}

Schema de sortie

L'API renvoie une réponse de prédiction avec les URL des résultats générés.

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”).

Exemple de réponse

{
  "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 intègre plus de 400 modèles d'IA directement dans votre assistant de codage IA. Une seule commande pour installer, puis utilisez le langage naturel pour générer des images, des vidéos et discuter avec des LLM.

Clients pris en charge

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ clients pris en charge

Installer

npx skills add AtlasCloudAI/atlas-cloud-skills

Configurer la clé API

Obtenez votre clé API depuis le tableau de bord Atlas Cloud et définissez-la comme variable d'environnement.

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

Fonctionnalités

Une fois installé, vous pouvez utiliser le langage naturel dans votre assistant IA pour accéder à tous les modèles Atlas Cloud.

Génération d'imagesGénérez des images avec des modèles comme Nano Banana 2, Z-Image, et plus encore.

Création de vidéosCréez des vidéos à partir de texte ou d'images avec Kling, Vidu, Veo, etc.

Chat LLMDiscutez avec Qwen, DeepSeek et d'autres grands modèles de langage.

Téléversement de médiasTéléversez des fichiers locaux pour l'édition d'images et les workflows image-vers-vidéo.

En savoir plus

github.com/AtlasCloudAI/atlas-cloud-skills

Serveur MCP

Le serveur MCP Atlas Cloud connecte votre IDE avec plus de 400 modèles d'IA via le Model Context Protocol. Compatible avec tout client compatible MCP.

Clients pris en charge

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ clients pris en charge

Installer

npx -y atlascloud-mcp

Configuration

Ajoutez la configuration suivante au fichier de paramètres MCP de votre IDE.

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

Outils disponibles

atlas_generate_imageGénérez des images à partir de prompts textuels.

atlas_generate_videoCréez des vidéos à partir de texte ou d'images.

atlas_chatDiscutez avec de grands modèles de langage.

atlas_list_modelsParcourez plus de 400 modèles d'IA disponibles.

atlas_quick_generateCréation de contenu en une étape avec sélection automatique du modèle.

atlas_upload_mediaTéléversez des fichiers locaux pour les workflows API.

En savoir plus

github.com/AtlasCloudAI/mcp-server

Schéma 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"
      }
    }
  }
}

Template de Prompt pour 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)

Aucun exemple disponible

Chargement...

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

Entrée

Sortie

Paramètres

Exemple de code

Installer

Authentification

En-têtes HTTP

Soumettre une requête

Soumettre une requête

Corps de la requête

Réponse

Vérifier le statut

Exemple d'interrogation

Valeurs de statut

Réponse terminée

Téléverser des fichiers

Exemple de téléversement

Réponse

Schema d'entrée

Exemple de corps de requête

Schema de sortie

Exemple de réponse

Atlas Cloud Skills

Clients pris en charge

Installer

Configurer la clé API

Fonctionnalités

Serveur MCP

Clients pris en charge

Installer

Configuration

Outils disponibles

Schéma API

Template de Prompt pour LLM

Key Features

Use Cases

Accelerated Inference

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Une seule API pour toute l'IA multimédia.

Entrée

Sortie

Paramètres

Exemple de code

Installer

Authentification

En-têtes HTTP

Soumettre une requête

Soumettre une requête

Corps de la requête

Réponse

Vérifier le statut

Exemple d'interrogation

Valeurs de statut

Réponse terminée

Téléverser des fichiers

Exemple de téléversement

Réponse

Schema d'entrée

Exemple de corps de requête

Schema de sortie

Exemple de réponse

Atlas Cloud Skills

Clients pris en charge