Open and Advanced Large-Scale Video Generative Models.
Entrada
Saída
InativoCada execução custará 0.032. Com $10 você pode executar aproximadamente 312 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": "alibaba/wan-2.2-spicy/video-extend",
"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": "alibaba/wan-2.2-spicy/video-extend",
"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": "alibaba/wan-2.2-spicy/video-extend"
}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 LoginWan 2.2: Open and Advanced Large-Scale Video Generative Model by Alibaba Wanxiang
Model Card Overview
| Field | Description |
|---|---|
| Model Name | Wan 2.2 |
| Developed by | Alibaba Tongyi Wanxiang Lab |
| Release Date | July 28, 2025 |
| Model Type | Video Generation |
| Related Links | GitHub: https://github.com/Wan-Video/Wan2.2, Hugging Face: https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B, Paper (arXiv): https://arxiv.org/abs/2503.20314 |
Introduction
Wan 2.2 is a significant upgrade to the Wan series of foundational video models, designed to push the boundaries of generative AI in video creation. The primary goal of Wan 2.2 is to provide an open and advanced suite of tools for generating high-quality, cinematic videos from various inputs, including text, images, and audio. Its core contribution lies in making state-of-the-art video generation technology accessible to a broader community of researchers and creators through open-sourcing its models and code. The project emphasizes cinematic aesthetics, complex motion generation, and computational efficiency, introducing several key innovations to achieve these aims.
Key Features & Innovations
Wan 2.2 introduces several groundbreaking features that set it apart from previous models:
-
Effective MoE Architecture: Wan 2.2 is the first model to successfully integrate a Mixture-of-Experts (MoE) architecture into a video diffusion model. This design uses specialized expert models for different stages of the denoising process, which significantly increases the model's capacity without raising computational costs. The model has a total of 27B parameters, but only 14B are active during any given step.
-
Cinematic-Level Aesthetics: The model was trained on a meticulously curated dataset with detailed labels for cinematic properties like lighting, composition, and color tone. This allows users to generate videos with precise and controllable artistic styles, achieving a professional, cinematic look.
-
Complex Motion Generation: By training on a vastly expanded dataset (+65.6% more images and +83.2% more videos compared to Wan 2.1), Wan 2.2 demonstrates a superior ability to generate complex and realistic motion. It shows enhanced generalization across various motions, semantics, and aesthetics.
-
Efficient High-Definition Video: The suite includes a highly efficient 5B model (TI2V-5B) that utilizes an advanced VAE for high-compression video generation. It can produce 720p video at 24 fps and is capable of running on consumer-grade GPUs like the NVIDIA RTX 4090, making high-definition AI video generation more accessible.
Model Architecture & Technical Details
The architecture of Wan 2.2 is built upon the Diffusion Transformer (DiT) paradigm and incorporates several key technical advancements.
Core Architecture
The primary models in the Wan 2.2 suite, such as the T2V-A14B, employ a Mixture-of-Experts (MoE) architecture. This framework consists of two main expert models:
- High-Noise Expert: Activated during the initial stages of the denoising process, this expert focuses on establishing the overall structure and layout of the video.
- Low-Noise Expert: Activated in the later stages, this expert is responsible for refining the details, textures, and fine-grained motion of the video.
The transition between these experts is dynamically determined by the signal-to-noise ratio (SNR) during generation. This MoE design allows the model to have a large parameter count (27B total) while keeping the number of active parameters (14B) and computational load comparable to smaller models.
Key Parameters & Variants
Wan 2.2 is offered in several variants, each tailored for different tasks and computational resources.
| Model Variant | Total Parameters | Key Feature | Supported Tasks |
|---|---|---|---|
| T2V-A14B | ~27B (14B active) | MoE for Text-to-Video | Text-to-Video |
| I2V-A14B | ~27B (14B active) | MoE for Image-to-Video | Image-to-Video |
| TI2V-5B | 5B | High-Compression VAE | Text-to-Video, Image-to-Video |
| S2V-14B | ~27B (14B active) | MoE for Speech-to-Video | Speech-to-Video |
| Animate-14B | ~27B (14B active) | MoE for Animation | Character Animation & Replacement |
Intended Use & Applications
Wan 2.2 is designed for a wide range of creative and academic applications. Its various models support a comprehensive set of downstream tasks, making it a versatile tool for digital artists, filmmakers, researchers, and developers.
- Cinematic Video Production: Generating high-fidelity video clips with specific artistic styles for short films, advertisements, or social media content.
- Storyboarding and Pre-visualization: Quickly creating video mockups from text descriptions or still images to visualize scenes.
- Character Animation: Animating static character images or replacing characters in existing videos with new ones while preserving motion and expression.
- Audio-Driven Content: Producing videos that are synchronized with speech or other audio tracks, suitable for creating animated avatars or visualizing audio content.
- Academic Research: Serving as a powerful, open-source foundation model for researchers exploring advancements in video generation, AI ethics, and multimodal AI.
- Creative Content Generation: Enabling artists and creators to explore new forms of digital art and storytelling by combining text, images, and audio to produce unique video content.
