alibaba/wan-2.5/image-to-video-fast

Hình ảnh-Video

Wan 2.5 Image-to-Video Fast API by Alibaba

alibaba/wan-2.5/image-to-video-fast

Image-to-video-fast

Get animated visuals from your images faster without major quality sacrifice. Perfect for preview workflows, previews at scale, or mass production of animated assets.

Đầu vào

Đang tải cấu hình tham số...

Đầu ra

Nhàn rỗi

Video đã tạo của bạn sẽ xuất hiện ở đây

Cấu hình tham số và nhấp Chạy để bắt đầu tạo

Mỗi lần chạy có giá $0.071. Với $10, bạn có thể chạy khoảng 140 lần.

Bạn có thể tiếp tục với:

Seedance 2.0 Kling v3 Vidu Wan2.7

Tham số

Ví dụ mã
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()

Cài đặt

Cài đặt gói cần thiết cho ngôn ngữ lập trình của bạn.

bash

pip install requests

Xác thực

Tất cả các yêu cầu API đều cần xác thực thông qua khóa API. Bạn có thể lấy khóa API từ bảng điều khiển Atlas Cloud.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Bảo mật khóa API của bạn

Không bao giờ để lộ khóa API trong mã phía máy khách hoặc kho lưu trữ công khai. Thay vào đó, hãy sử dụng biến môi trường hoặc proxy phía máy chủ.

Gửi yêu cầu

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())

Gửi yêu cầu

Gửi yêu cầu tạo nội dung bất đồng bộ. API trả về một ID dự đoán mà bạn có thể sử dụng để kiểm tra trạng thái và lấy kết quả.

POST/api/v1/model/generateVideo

Nội dung yêu cầu

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']}")

Phản hồi

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Kiểm tra trạng thái

Truy vấn endpoint dự đoán để kiểm tra trạng thái hiện tại của yêu cầu.

GET/api/v1/model/prediction/{prediction_id}

Ví dụ truy vấn

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)

Giá trị trạng thái

processingYêu cầu vẫn đang được xử lý.

completedQuá trình tạo đã hoàn tất. Kết quả đầu ra đã sẵn sàng.

succeededQuá trình tạo thành công. Kết quả đầu ra đã sẵn sàng.

failedQuá trình tạo thất bại. Kiểm tra trường lỗi.

Phản hồi hoàn tất

{
  "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"
  }
}

Tải tệp lên

Tải tệp lên bộ nhớ Atlas Cloud và nhận URL mà bạn có thể sử dụng trong các yêu cầu API của mình. Sử dụng multipart/form-data để tải lên.

POST/api/v1/model/uploadMedia

Ví dụ tải lên

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}")

Phản hồi

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

Các tham số sau được chấp nhận trong nội dung yêu cầu.

Tổng cộng: 0Bắt buộc: 0Tùy chọn: 0

Không có tham số nào.

Ví dụ nội dung yêu cầu

json

{
  "model": "alibaba/wan-2.5/image-to-video-fast"
}

Output Schema

API trả về phản hồi dự đoán với các URL đầu ra đã tạo.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for video generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Ví dụ phản hồi

json

{
  "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 tích hợp hơn 300 mô hình AI trực tiếp vào trợ lý lập trình AI của bạn. Một lệnh để cài đặt, sau đó sử dụng ngôn ngữ tự nhiên để tạo hình ảnh, video và trò chuyện với LLM.

Ứng dụng được hỗ trợ

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ ứng dụng được hỗ trợ

Cài đặt

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Thiết lập khóa API

Lấy khóa API từ bảng điều khiển Atlas Cloud và đặt nó làm biến môi trường.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Khả năng

Sau khi cài đặt, bạn có thể sử dụng ngôn ngữ tự nhiên trong trợ lý AI để truy cập tất cả các mô hình Atlas Cloud.

Tạo hình ảnhTạo hình ảnh với các mô hình như Nano Banana 2, Z-Image và nhiều hơn nữa.

Tạo videoTạo video từ văn bản hoặc hình ảnh với Kling, Vidu, Veo, v.v.

Trò chuyện LLMTrò chuyện với Qwen, DeepSeek và các mô hình ngôn ngữ lớn khác.

Tải lên phương tiệnTải tệp cục bộ lên để chỉnh sửa hình ảnh và quy trình chuyển hình ảnh sang video.

Tìm hiểu thêm

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server kết nối IDE của bạn với hơn 300 mô hình AI thông qua Model Context Protocol. Hoạt động với bất kỳ ứng dụng tương thích MCP nào.

Ứng dụng được hỗ trợ

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ ứng dụng được hỗ trợ

Cài đặt

bash

npx -y atlascloud-mcp

Cấu hình

Thêm cấu hình sau vào tệp cài đặt MCP của IDE.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Công cụ khả dụng

atlas_generate_imageTạo hình ảnh từ mô tả văn bản.

atlas_generate_videoTạo video từ văn bản hoặc hình ảnh.

atlas_chatTrò chuyện với các mô hình ngôn ngữ lớn.

atlas_list_modelsDuyệt hơn 300 mô hình AI khả dụng.

atlas_quick_generateTạo nội dung một bước với tự động chọn mô hình.

atlas_upload_mediaTải tệp cục bộ lên cho quy trình API.

Tìm hiểu thêm

github.com/AtlasCloudAI/mcp-server

Schema API

Schema không khả dụng

Đăng nhập để xem lịch sử yêu cầu

Bạn cần đăng nhập để truy cập lịch sử yêu cầu mô hình của mình.

Đăng nhập

Seedance 1.5 Pro

TẠO NỘI DUNG NGHE NHÌN GỐC

Âm Thanh và Hình Ảnh, Tất Cả Trong Một Lần Quay

Mô hình AI đột phá của ByteDance tạo ra âm thanh và video đồng bộ hoàn hảo cùng lúc từ một quy trình thống nhất duy nhất. Trải nghiệm tạo nội dung nghe nhìn gốc thực sự với đồng bộ môi chính xác đến mili giây trên hơn 8 ngôn ngữ.

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

Bottom line: Bottom line: Creation has never been so simple, fast, and smart - Wan 2.5 is your secret weapon for marketing!

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

Bottom line: Bottom line: Cross-border content creation has never been so simple, fast, and smart.

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

Bottom line: Bottom line: Wan 2.5 makes creation easier, freer, and more exciting with every attempt!

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

Bottom line: Bottom line: Wan 2.5 makes creation effortless, efficient, and free - every attempt is spectacular!

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.

Digital Human Sync

Study Room Scholar

Middle-aged man reading with perfect lip-sync in a warm study environment

Lip-sync with audioEnvironmental soundsCharacter emotion

Prompt

A 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.

Dual Character Scene

Park Sunset Romance

Couple interaction with synchronized dual character actions and expressions

Dual character syncNatural interactionAmbient soundscape

Prompt

A 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.

Character Restoration

Ballet Performance Art

Precise character trait restoration with artistic movement and expression

Character trait restorationMovement precisionArtistic lighting

Prompt

A 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.

Artistic Style Rendering

Ghibli Forest Magic

Studio Ghibli-inspired animation with hand-painted watercolor texture

Ghibli art styleHand-painted textureMagical atmosphere

Prompt

Studio 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.

Experience these prompts and discover the creative potential of Wan 2.5's synchronized A/V generation technology.

Hoàn Hảo Cho

🎬

Video Production

📢

Marketing Content

🎓

Educational Videos

📱

Social Media

🌐

Multilingual Content

💼

Corporate Training

🎭

Entertainment

💃

Performance Art

🎨

Animation & Anime

📚

Storytelling

👥

Dual Character Videos

🎙️

Interviews

📺

Broadcast Media

Thông Số Kỹ Thuật

Model Type:Audio-Visual Synchronized Generation

Key Features:A/V sync, Character restoration, Artistic rendering, Multi-language

Language Support:Chinese, English, and more

Output Quality:Professional HD video with audio

Generation Speed:Fast one-step generation

API Integration:RESTful API with comprehensive documentation

Trải Nghiệm Tạo Nội Dung Nghe Nhìn Gốc

Tham gia cùng các nhà làm phim, nhà quảng cáo và người sáng tạo trên toàn thế giới đang cách mạng hóa việc tạo nội dung video với công nghệ đột phá của Seedance 1.5 Pro.

🎬One-Step A/V Sync

🌍Multilingual Support

⚡Cost Effective

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.

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