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.
Entrada
Saída
InativoCada execução custará $0.026. Com $10 você pode executar aproximadamente 384 vezes.
Você pode continuar com:
Exemplo de código
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()Instalar
Instale o pacote necessário para a sua linguagem de programação.
pip install requestsAutenticação
Todas as solicitações de API requerem autenticação por meio de uma chave de API. Você pode obter sua chave de API no painel do Atlas Cloud.
export ATLASCLOUD_API_KEY="your-api-key-here"Cabeçalhos HTTP
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}Nunca exponha sua chave de API em código do lado do cliente ou repositórios públicos. Use variáveis de ambiente ou um proxy de backend.
Enviar uma solicitação
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())Enviar uma solicitação
Envie uma solicitação de geração assíncrona. A API retorna um ID de predição que você pode usar para verificar o status e obter o resultado.
/api/v1/model/generateVideoCorpo da solicitação
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']}")Resposta
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Verificar status
Consulte o endpoint de predição para verificar o status atual da sua solicitação.
/api/v1/model/prediction/{prediction_id}Exemplo de polling
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)Valores de status
processingA solicitação ainda está sendo processada.completedA geração está completa. As saídas estão disponíveis.succeededA geração foi bem-sucedida. As saídas estão disponíveis.failedA geração falhou. Verifique o campo de erro.Resposta concluída
{
"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"
}
}Enviar arquivos
Envie arquivos para o armazenamento do Atlas Cloud e obtenha uma URL que pode ser usada nas suas solicitações de API. Use multipart/form-data para enviar.
/api/v1/model/uploadMediaExemplo de upload
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}")Resposta
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Schema de entrada
Os seguintes parâmetros são aceitos no corpo da solicitação.
Nenhum parâmetro disponível.
Exemplo de corpo da solicitação
{
"model": "vidu/q2-turbo/start-end-to-video"
}Schema de saída
A API retorna uma resposta de predição com as URL de saída geradas.
Exemplo de resposta
{
"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
O Atlas Cloud Skills integra mais de 300 modelos de IA diretamente no seu assistente de codificação com IA. Um comando para instalar e depois use linguagem natural para gerar imagens, vídeos e conversar com LLM.
Clientes compatíveis
Instalar
npx skills add AtlasCloudAI/atlas-cloud-skillsConfigurar chave de API
Obtenha sua chave de API no painel do Atlas Cloud e defina-a como variável de ambiente.
export ATLASCLOUD_API_KEY="your-api-key-here"Funcionalidades
Após a instalação, você pode usar linguagem natural no seu assistente de IA para acessar todos os modelos do Atlas Cloud.
MCP Server
O Atlas Cloud MCP Server conecta seu IDE com mais de 300 modelos de IA através do Model Context Protocol. Funciona com qualquer cliente compatível com MCP.
Clientes compatíveis
Instalar
npx -y atlascloud-mcpConfiguração
Adicione a seguinte configuração ao arquivo de configuração de MCP do seu IDE.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Ferramentas disponíveis
API Schema
Schema não disponívelFaça login para ver o histórico de solicitações
Você precisa fazer login para acessar o histórico de solicitações do modelo.
Fazer LoginVidu 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.
