alibaba/wan-2.5/text-to-image

Generate AI images with Alibaba WAN 2.5 text-to-image model.

TEXT-TO-IMAGEHOTNEW
Wan-2.5 Text-to-image
텍스트를 이미지로

Generate AI images with Alibaba WAN 2.5 text-to-image model.

입력

매개변수 구성 로드 중...

출력

대기
생성된 이미지가 여기에 표시됩니다
설정을 구성하고 실행을 클릭하여 시작하세요

요청당 0.021가 소요됩니다. $10로 이 모델을 약 476번 실행할 수 있습니다.

다음으로 할 수 있는 작업:

파라미터

코드 예시

import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "alibaba/wan-2.5/text-to-image",
    "prompt": "A beautiful landscape with mountains and lake",
    "width": 512,
    "height": 512,
    "steps": 20,
    "guidance_scale": 7.5,
}

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"] == "completed":
            print("Generated image:", 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)

image_url = check_status()

설치

사용하는 언어에 필요한 패키지를 설치하세요.

bash
pip install requests

인증

모든 API 요청에는 API 키를 통한 인증이 필요합니다. Atlas Cloud 대시보드에서 API 키를 받을 수 있습니다.

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

HTTP 헤더

python
import os

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

클라이언트 측 코드나 공개 저장소에 API 키를 노출하지 마세요. 대신 환경 변수 또는 백엔드 프록시를 사용하세요.

요청 제출

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
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())

요청 제출

비동기 생성 요청을 제출합니다. API는 상태 확인 및 결과 조회에 사용할 수 있는 예측 ID를 반환합니다.

POST/api/v1/model/generateImage

요청 본문

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "alibaba/wan-2.5/text-to-image",
    "input": {
        "prompt": "A beautiful landscape with mountains and lake"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

응답

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

상태 확인

예측 엔드포인트를 폴링하여 요청의 현재 상태를 확인합니다.

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

폴링 예시

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)

상태 값

processing요청이 아직 처리 중입니다.
completed생성이 완료되었습니다. 출력을 사용할 수 있습니다.
succeeded생성이 성공했습니다. 출력을 사용할 수 있습니다.
failed생성에 실패했습니다. 오류 필드를 확인하세요.

완료 응답

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

파일 업로드

Atlas Cloud 스토리지에 파일을 업로드하고 API 요청에 사용할 수 있는 URL을 받습니다. multipart/form-data를 사용하여 업로드합니다.

POST/api/v1/model/uploadMedia

업로드 예시

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}")

응답

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

입력 Schema

다음 매개변수가 요청 본문에서 사용 가능합니다.

전체: 0필수: 0선택: 0

사용 가능한 매개변수가 없습니다.

요청 본문 예시

json
{
  "model": "alibaba/wan-2.5/text-to-image"
}

출력 Schema

API는 생성된 출력 URL이 포함된 예측 응답을 반환합니다.

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 image 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

응답 예시

json
{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills는 300개 이상의 AI 모델을 AI 코딩 어시스턴트에 직접 통합합니다. 한 번의 명령으로 설치하고 자연어로 이미지, 동영상 생성 및 LLM과 대화할 수 있습니다.

지원 클라이언트

Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ 지원 클라이언트

설치

bash
npx skills add AtlasCloudAI/atlas-cloud-skills

API 키 설정

Atlas Cloud 대시보드에서 API 키를 받아 환경 변수로 설정하세요.

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

기능

설치 후 AI 어시스턴트에서 자연어를 사용하여 모든 Atlas Cloud 모델에 접근할 수 있습니다.

이미지 생성Nano Banana 2, Z-Image 등의 모델로 이미지를 생성합니다.
동영상 제작Kling, Vidu, Veo 등으로 텍스트나 이미지에서 동영상을 만듭니다.
LLM 채팅Qwen, DeepSeek 등 대규모 언어 모델과 대화합니다.
미디어 업로드이미지 편집 및 이미지-동영상 변환 워크플로우를 위해 로컬 파일을 업로드합니다.

MCP Server

Atlas Cloud MCP Server는 Model Context Protocol을 통해 IDE와 300개 이상의 AI 모델을 연결합니다. MCP 호환 클라이언트에서 사용할 수 있습니다.

지원 클라이언트

Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ 지원 클라이언트

설치

bash
npx -y atlascloud-mcp

설정

다음 설정을 IDE의 MCP 설정 파일에 추가하세요.

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

사용 가능한 도구

atlas_generate_image텍스트 프롬프트로 이미지를 생성합니다.
atlas_generate_video텍스트나 이미지로 동영상을 만듭니다.
atlas_chat대규모 언어 모델과 대화합니다.
atlas_list_models300개 이상의 사용 가능한 AI 모델을 탐색합니다.
atlas_quick_generate자동 모델 선택으로 원스텝 콘텐츠 생성.
atlas_upload_mediaAPI 워크플로우를 위해 로컬 파일을 업로드합니다.

API 스키마

스키마를 사용할 수 없음

요청 기록을 보려면 로그인하세요

모델 요청 기록에 액세스하려면 로그인해야 합니다.

로그인

Seedance 1.5 Pro

네이티브 오디오-비주얼 동기화 생성

사운드와 비전, 원테이크로 완벽 동기화

ByteDance의 혁신적인 AI 모델로 단일 통합 프로세스에서 완벽하게 동기화된 오디오와 비디오를 동시에 생성합니다. 8개 이상의 언어에서 밀리초 단위 정밀도의 립싱크를 제공하는 진정한 네이티브 오디오-비주얼 생성을 경험하세요.

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!

핵심 기능

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.

완벽한 활용

🎬
Video Production
📢
Marketing Content
🎓
Educational Videos
📱
Social Media
🌐
Multilingual Content
💼
Corporate Training
🎭
Entertainment
💃
Performance Art
🎨
Animation & Anime
📚
Storytelling
👥
Dual Character Videos
🎙️
Interviews
📺
Broadcast Media

기술 사양

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

네이티브 오디오-비주얼 생성 경험

Seedance 1.5 Pro의 획기적인 기술로 비디오 콘텐츠 제작을 혁신하고 있는 전 세계 영화 제작자, 광고주, 크리에이터들과 함께하세요.

🎬One-Step A/V Sync
🌍Multilingual Support
Cost Effective

Alibaba WAN 2.5 Text-to-Image Model

Alibaba WAN 2.5 is a high-quality text-to-image model provided by Alibaba Cloud's DashScope platform.

300개 이상의 모델로 시작하세요,

모든 모델 탐색