Wan 2.5 Image-to-Video Fast API by Alibaba
Get animated visuals from your images faster without major quality sacrifice. Perfect for preview workflows, previews at scale, or mass production of animated assets.
Entrada
Saída
InativoCada execução custará $0.071. Com $10 você pode executar aproximadamente 140 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-2.5/image-to-video-fast",
"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-2.5/image-to-video-fast",
"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-2.5/image-to-video-fast"
}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 LoginSeedance 1.5 Pro
GERAÇÃO NATIVA ÁUDIO-VISUALSom e Imagem, Tudo em Uma Única Tomada
O modelo de IA revolucionário da ByteDance que gera áudio e vídeo perfeitamente sincronizados simultaneamente a partir de um único processo unificado. Experimente a verdadeira geração nativa áudio-visual com sincronização labial de precisão milimétrica em mais de 8 idiomas.
Why Choose Wan 2.5?
More Affordable
Despite Google's recent price cuts, Veo 3 remains expensive overall. Wan 2.5 is lightweight and cost-effective, providing creators with more options while significantly reducing production costs.
One-Step Generation, End-to-End Sync
With Wan 2.5, no separate voice recording or manual lip alignment is needed. Just provide a clear, structured prompt to generate complete videos with audio/voiceover and lip sync in one go - faster and simpler.
Multilingual Friendly
When prompts are in Chinese, Wan 2.5 reliably generates A/V synchronized videos. In contrast, Veo 3 often displays "unknown language" for Chinese prompts.
Precise Character Recreation
Wan 2.5 excels at character trait restoration, accurately presenting character appearance, expressions, and movement styles, making generated video characters more recognizable and personalized for enhanced storytelling and immersion.
Artistic Style Rendering
Supports Studio Ghibli-style rendering, creating hand-painted watercolor textures and animation effects. Brings warm, dreamy visual experiences that enhance artistic appeal and storytelling depth.
Who Can Benefit?
Marketing Teams
Whether it's product launches, promotional campaigns, or brand marketing, Wan 2.5 helps you quickly generate high-quality videos, making creation easy and efficient.
- Product demos and tutorials without coordination headaches
- Social media marketing with multilingual subtitles and lip sync
- AI-generated content lets teams focus on strategy and creativity
Global Enterprises
Provides ideal content localization solutions for multinational companies, making creation easier and more efficient.
- Multilingual video support with prompt recognition
- One-click generation of lip-synced subtitles and voiceovers
- Fast content localization for global markets
Story Creators / YouTubers
Creators can leverage Wan 2.5 to improve video production efficiency while ensuring high-quality output.
- Immersive storytelling with precise character actions and expressions
- Higher publishing efficiency with reduced editing and post-production time
- Diverse content from short videos to animated story segments
Corporate Training Teams
Wan 2.5 makes corporate training more efficient and engaging.
- Professional videos replace boring text documents
- Quick creation of operational demos and training tutorials
- Consistent style and standardized output for global rollout
Creative Freelancers / Small Studios
Wan 2.5 lets creativity flow without expensive equipment or actors - AI generates everything efficiently.
- Experiment with diverse works from short films to social media content
- From inspiration to completion with "one-click generation"
- High-quality content without expensive equipment or professional actors
Educational Institutions / Online Course Creators
Transform creativity into reality without high costs - Wan 2.5 makes quality content production easy and economical.
- Experiment with various styles from short films to promotional videos
- Higher production efficiency from concept to finished product
- Quality content without expensive equipment or professional talent
Core Features
One-Step A/V Generation
Generate complete videos with synchronized audio, voiceover, and lip-sync in a single process
Dual Character Sync
Supports simultaneous generation of two characters with synchronized actions, expressions, and lip-sync for natural interactions
Professional Quality
High-quality video output with realistic character expressions and precise lip synchronization
Multilingual Support
Excellent support for Chinese prompts and reliable generation of multilingual content
Cost Effective
Significantly lower costs compared to competitors while maintaining professional quality
Character Trait Restoration
Precisely recreates character appearance, expressions, and movement styles with high fidelity and personality
Artistic Style Rendering
Supports various artistic styles including Studio Ghibli-inspired hand-painted watercolor textures
Immersive Scenes
Perfect for dialogue scenes, interviews, or dual-person short films with natural audio-visual consistency
Wan 2.5 Prompt Showcase
Discover the power of Wan 2.5 through these curated examples. From digital human lip-sync to dual character scenes, artistic rendering to character restoration - experience the possibilities.
Study Room Scholar
Middle-aged man reading with perfect lip-sync in a warm study environmentA middle-aged man sitting at a wooden desk in a cozy study room, surrounded by bookshelves and a warm lamp glow. He opens an old book and reads aloud with a calm, deep voice: 'History teaches us more than just facts… it shows us who we are.' The room has subtle background sounds: pages turning, the faint ticking of a clock, and distant rain against the window.
Park Sunset Romance
Couple interaction with synchronized dual character actions and expressionsA young couple sitting on a park bench during sunset. The woman leans her head on the man's shoulder. He whispers softly: 'No matter where we go, I'll always be here with you.' The sound includes the rustling of leaves, distant laughter of children playing, and the gentle hum of cicadas in the evening air.
Ballet Performance Art
Precise character trait restoration with artistic movement and expressionA graceful ballerina with her hair in a messy bun, performing a powerful and emotional contemporary ballet routine. She is in a minimalist, dark art studio. Abstract patterns of light and shadow, projected from a hidden source, dance across her body and the surrounding walls, constantly shifting with her movements. The camera focuses on the tension in her muscles and the expressive gestures of her hands. A single, dramatic slow-motion shot captures her mid-air leap, with the light patterns swirling around her like a galaxy. Moody, artistic, high contrast.
Ghibli Forest Magic
Studio Ghibli-inspired animation with hand-painted watercolor textureStudio Ghibli-inspired anime style. A young girl with a straw hat lies peacefully in a sun-dappled magical forest, surrounded by friendly, glowing forest spirits (Kodama). A gentle breeze rustles the leaves of the giant, ancient trees. The air is filled with sparkling dust motes, illuminated by shafts of sunlight. The art style is soft, with a hand-painted watercolor texture. The scene feels serene, magical, and heartwarming.
Perfeito Para
Especificações Técnicas
Experimente a Geração Nativa Áudio-Visual
Junte-se a cineastas, anunciantes e criadores de todo o mundo que estão revolucionando a criação de conteúdo de vídeo com a tecnologia inovadora do Seedance 1.5 Pro.
Wan 2.5: A next-generation AI video generation model developed by Alibaba Wanxiang.
Model Card Overview
| Field | Description |
|---|---|
| Model Name | Wan 2.5 |
| Developed By | Alibaba Group |
| Release Date | September 24, 2025 |
| Model Type | Generative AI, Video Foundation Model |
| Related Links | Official Website: https://wan.video/, Hugging Face: https://huggingface.co/Wan-AI, Technical Paper (Wan Series): https://arxiv.org/abs/2503.20314 |
Introduction
Wan 2.5 is a state-of-the-art, open-source video foundation model developed by Alibaba's Wan AI team. It is designed to generate high-quality, cinematic videos complete with synchronized audio directly from text or image prompts. The model represents a significant advancement in the field of generative AI, aiming to lower the barrier for creative video production. Its core contribution lies in its ability to produce coherent, dynamic, and narratively consistent video clips with a high degree of realism and integrated audio-visual elements, such as lip-sync and sound effects, in a single, streamlined process.
Key Features & Innovations
Wan 2.5 introduces several key features that distinguish it from previous models and competitors:
- Unified Audio-Visual Synthesis: Unlike many models that require separate steps for video and audio generation, Wan 2.5 creates video with natively synchronized audio, including voice, sound effects, and lip-sync, in one step.
- High-Fidelity, High-Resolution Output: The model is capable of generating videos in multiple resolutions, including 480p, 720p, and full 1080p HD, with significant improvements in visual quality and frame-to-frame stability over its predecessors.
- Extended Video Duration: Wan 2.5 can generate video clips up to 10 seconds in length, offering more creative flexibility for storytelling compared to other models in its class.
- Advanced Cinematic Control: The model demonstrates a sophisticated understanding of cinematic language, allowing for precise control over camera movement, shot composition, and character consistency within scenes.
- Open-Source Commitment: Following the precedent set by earlier versions, the Wan series of models, including Wan 2.5, are open-sourced to encourage research, development, and innovation within the broader AI community.
Model Architecture & Technical Details
Wan 2.5 is built upon the Diffusion Transformer (DiT) paradigm, which has become a mainstream approach for high-quality generative tasks. The technical report for the Wan model series outlines a suite of innovations that contribute to its performance.
The architecture includes a novel Variational Autoencoder (VAE) designed for high-efficiency video compression, enabling the model to handle high-resolution video data effectively. The Wan series is available in multiple sizes to balance performance and computational requirements, such as the 1.3B and 14B parameter models detailed for Wan 2.2. The model was trained on a massive, curated dataset comprising billions of images and videos, which enhances its ability to generalize across a wide range of motions, semantics, and aesthetic styles.
Intended Use & Applications
Wan 2.5 is designed for a wide array of applications in creative and commercial fields. Its intended uses include:
- Content Creation: Generating short-form videos for social media, marketing campaigns, and digital advertising.
- Storytelling and Filmmaking: Creating cinematic scenes, character animations, and narrative sequences for short films and conceptual art.
- Prototyping: Rapidly visualizing scripts and storyboards for film, television, and game development.
- Personalized Media: Enabling users to create unique, personalized video content from their own ideas and images.
Performance
Wan 2.5 has demonstrated significant performance improvements over previous versions and holds a competitive position against other leading video generation models. Independent reviews and benchmarks provide insight into its capabilities.
Benchmark Scores
A review conducted by Curious Refuge Labs™ evaluated the model's visual generation capabilities across several metrics.
| Metric | Score (out of 10) |
|---|---|
| Prompt Adherence | 7.0 |
| Temporal Consistency | 6.6 |
| Visual Fidelity | 6.5 |
| Motion Quality | 5.9 |
| Style & Cinematic Realism | 5.7 |
| Overall Score | 6.3 |
These scores indicate strong prompt understanding and a notable improvement in visual quality from Wan 2.2, although it still shows limitations in complex motion and realism compared to top-tier commercial models.
