Vidu Q2-Turbo Start-end-to-Video is an advanced AI video generation model that brings static images to life. Upload a reference image and describe the motion you want — the model generates high-quality video with smooth animation, optional audio, and cinematic quality up to 1080p.
Entrée
Sortie
InactifVotre requête coûtera $0.026 par exécution. Avec $10, vous pouvez exécuter ce modèle environ 384 fois.
Vous pouvez continuer avec :
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/q2-turbo/start-end-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()Installer
Installez le package requis pour votre langage.
pip install requestsAuthentification
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}"
}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.
/api/v1/model/generateVideoCorps 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/q2-turbo/start-end-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']}")Réponse
{
"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.
/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"
}
}Télécharger des fichiers
Téléchargez des fichiers vers le stockage Atlas Cloud et obtenez une URL utilisable dans vos requêtes API. Utilisez multipart/form-data pour le téléchargement.
/api/v1/model/uploadMediaExemple de téléchargement
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.
Aucun paramètre disponible.
Exemple de corps de requête
{
"model": "vidu/q2-turbo/start-end-to-video"
}Schema de sortie
L'API renvoie une réponse de prédiction avec les URL des résultats générés.
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 300 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
Installer
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigurer 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.
Serveur MCP
Le serveur MCP Atlas Cloud connecte votre IDE avec plus de 300 modèles d'IA via le Model Context Protocol. Compatible avec tout client compatible MCP.
Clients pris en charge
Installer
npx -y atlascloud-mcpConfiguration
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
Schéma API
Schéma non disponibleVeuillez vous connecter pour voir l'historique des requêtes
Vous devez vous connecter pour accéder à l'historique de vos requêtes de modèle.
Se ConnecterVidu Q2-Turbo Start-End-to-Video
Vidu Q2-Turbo Start-End-to-Video is the fastest start-end-to-video model in the Q2 family. Provide a start frame and an end frame, and the model generates a smooth video transition between them at ultra-low latency — built for high-volume pipelines, real-time previews, and cost-sensitive workflows where speed takes priority.
Why Choose This?
-
Ultra-fast generation The fastest start-end-to-video option in the Q2 lineup, optimized for throughput.
-
Precise frame control Define exactly where the video begins and ends with two reference images.
-
Affordable pricing The most cost-effective start-end-to-video option in the Q2 family.
-
High resolution output Generate videos in 540p, 720p, or 1080p quality.
-
Flexible duration Create videos from 1 to 10 seconds in length.
-
Audio generation Optional synchronized audio and background music.
Parameters
| Parameter | Required | Description |
|---|---|---|
| prompt | Yes | Text description of the transition, motion, and action between frames |
| start_image | Yes | The first frame image (URL or upload) |
| end_image | Yes | The last frame image (URL or upload) |
| resolution | No | Output quality: 540p, 720p (default), 1080p |
| duration | No | Video length in seconds (1-10, default: 5) |
| generate_audio | No | Generate synchronized audio (default: enabled) |
| bgm | No | Add background music (default: enabled) |
| seed | No | Random seed for reproducibility |
How to Use
- Upload your start image — provide the image representing the first frame of the video.
- Upload your end image — provide the image representing the last frame of the video.
- Write your prompt — describe the transition, motion, and how the scene evolves between frames.
- Set resolution — choose based on your speed and quality requirements.
- Adjust duration — set video length up to 10 seconds.
- Configure audio (optional) — enable/disable audio generation and background music.
- Run — submit and download your video.
Pricing
| Resolution | Cost |
|---|---|
| 540p | Starts at 0.0100/sec |
| 720p | Starts at 0.0500/sec |
| 1080p | Starts at 0.0500/sec |
Best Use Cases
- High-Volume Pipelines — Process large batches of frame-to-frame transitions quickly and affordably.
- Real-Time Previews — Generate fast transition previews before committing to higher-quality renders.
- Rapid Prototyping — Explore start-to-end motion concepts with near-instant turnaround.
- Cost-Sensitive Projects — Deliver transition video content within tight budget constraints.
- Developer Testing — Integrate start-end video generation into applications with minimal latency.
Pro Tips
- Use concise, action-focused prompts — the Turbo pipeline responds well to clear, direct transition instructions.
- Ensure start and end images share a compatible composition for the most natural transition output.
- Use 540p for rapid iteration and upgrade to 720p or 1080p for final deliverables.
- Keep subject framing consistent between start and end images for smoother interpolation.
- Enable generate_audio to add energy and context to the transition without extra effort.
- Use seed to lock in a result you like while experimenting with different prompt variations.
Notes
- All three fields — prompt, start_image, and end_image — are required.
- Maximum video duration is 10 seconds.
- The model interpolates motion and scene content between the two provided frames.
- Audio generation adds synchronized sound effects and ambient audio.
- BGM adds background music appropriate to the scene mood.
- Ensure uploaded image URLs are publicly accessible.
Related Models
- Vidu Q2-Turbo Image-to-Video — Ultra-fast video animation from a single reference image.
- Vidu Q2-Pro-Fast Start-End-to-Video — Faster Pro-quality transitions with 1080p support.
- Vidu Q2-Pro Start-End-to-Video — Maximum quality transitions with extended duration and full resolution range.
