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.
Girdi
Çıktı
BoştaHer çalıştırma $0.071 maliyete sahip. 10$ ile yaklaşık 140 kez çalıştırabilirsiniz.
Şununla devam edebilirsiniz:
Kod örneği
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()Kurulum
Programlama diliniz için gerekli paketi kurun.
pip install requestsKimlik Doğrulama
Tüm API istekleri, API anahtarı ile kimlik doğrulama gerektirir. API anahtarınızı Atlas Cloud kontrol panelinden alabilirsiniz.
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP Başlıkları
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}API anahtarınızı asla istemci tarafı kodunda veya herkese açık depolarda ifşa etmeyin. Bunun yerine ortam değişkenleri veya arka uç proxy kullanın.
İstek gönder
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())İstek Gönder
Asenkron bir oluşturma isteği gönderin. API, durumu kontrol etmek ve sonucu almak için kullanabileceğiniz bir tahmin ID'si döndürür.
/api/v1/model/generateVideoİstek Gövdesi
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']}")Yanıt
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}Durumu Kontrol Et
İsteğinizin mevcut durumunu kontrol etmek için tahmin uç noktasını sorgulayın.
/api/v1/model/prediction/{prediction_id}Sorgulama Örneği
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)Durum Değerleri
processingİstek hâlâ işleniyor.completedOluşturma tamamlandı. Çıktılar kullanılabilir.succeededOluşturma başarılı oldu. Çıktılar kullanılabilir.failedOluşturma başarısız oldu. Hata alanını kontrol edin.Tamamlanmış Yanıt
{
"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"
}
}Dosya Yükle
Dosyaları Atlas Cloud depolama alanına yükleyin ve API isteklerinizde kullanabileceğiniz bir URL alın. Yüklemek için multipart/form-data kullanın.
/api/v1/model/uploadMediaYükleme Örneği
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}")Yanıt
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}Input Schema
İstek gövdesinde aşağıdaki parametreler kabul edilir.
Kullanılabilir parametre yok.
Örnek İstek Gövdesi
{
"model": "alibaba/wan-2.5/image-to-video-fast"
}Output Schema
API, oluşturulan çıktı URL'lerini içeren bir tahmin yanıtı döndürür.
Örnek Yanıt
{
"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, 300'den fazla AI modelini doğrudan AI kodlama asistanınıza entegre eder. Kurmak için tek bir komut, ardından görüntü, video oluşturmak ve LLM ile sohbet etmek için doğal dil kullanın.
Desteklenen İstemciler
Kurulum
npx skills add AtlasCloudAI/atlas-cloud-skillsAPI Anahtarını Ayarla
API anahtarınızı Atlas Cloud kontrol panelinden alın ve ortam değişkeni olarak ayarlayın.
export ATLASCLOUD_API_KEY="your-api-key-here"Yetenekler
Kurulduktan sonra, tüm Atlas Cloud modellerine erişmek için AI asistanınızda doğal dil kullanabilirsiniz.
MCP Server
Atlas Cloud MCP Server, IDE'nizi Model Context Protocol aracılığıyla 300'den fazla AI modeline bağlar. Herhangi bir MCP uyumlu istemci ile çalışır.
Desteklenen İstemciler
Kurulum
npx -y atlascloud-mcpYapılandırma
Aşağıdaki yapılandırmayı IDE'nizin MCP ayarları dosyasına ekleyin.
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}Mevcut Araçlar
API Şeması
Şema mevcut değilİstek geçmişini görüntülemek için oturum açın
Model istek geçmişinize erişmek için oturum açmanız gerekir.
Oturum AçSeedance 1.5 Pro
YERLİ SES-GÖRSEL OLUŞTURMASes ve Görüntü, Tek Çekimde Hepsi
ByteDance'in tek birleşik süreçten eş zamanlı olarak mükemmel senkronize ses ve video üreten devrim niteliğindeki yapay zeka modeli. 8'den fazla dilde milisaniye hassasiyetinde dudak senkronizasyonu ile gerçek yerli ses-görsel üretimi deneyimleyin.
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.
Mükemmel Kullanım Alanları
Teknik Özellikler
Yerli Ses-Görsel Üretimi Deneyimleyin
Seedance 1.5 Pro'nun çığır açan teknolojisi ile video içerik oluşturmayı devrimleştiren dünya çapındaki film yapımcılarına, reklamverenlere ve içerik üreticilerine katılın.
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.
