General-purpose image generation model that supports various art styles and is particularly good at rendering complex text.
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/qwen-image/text-to-image-max",
"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 requests인증
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/generateImage요청 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/qwen-image/text-to-image-max",
"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/uploadMedia업로드 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/qwen-image/text-to-image-max"
}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.
completed_atstring
ISO 8601 timestamp when the prediction was completed.
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-skillsAPI 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-mcp설정
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 없음Alibaba Qwen-Image Text-to-Image Max
The flagship text-to-image generation model from Alibaba Cloud, designed to deliver state-of-the-art visual quality, exceptional prompt adherence, and rich artistic detail. Qwen-Image Max represents the pinnacle of the Qwen-Image family, capable of transforming complex text descriptions into stunning, high-resolution visuals suitable for professional and creative workflows.
Overview
- Purpose: Generate premium-quality images from natural language descriptions.
- Core Capability: Industry-leading visual fidelity with deep semantic understanding of prompts.
- Foundation: Built on Alibaba's advanced large-scale multi-modal architecture.
- Typical Output: High-resolution, photorealistic or artistic images with precise lighting, texture, and composition.
- Use Cases: Professional design, advertising creatives, concept art, marketing materials, and high-end content creation.
Key Features
- Superior Visual Quality: Delivers the highest level of detail, texture, and lighting realism available in the Qwen-Image series.
- Complex Prompt Understanding: Accurately interprets long, intricate prompts, including spatial relationships, artistic styles, and specific object attributes.
- Text Rendering: Enhanced capability to render legible text within generated images (e.g., signboards, posters).
- Style Versatility: Masterfully handles a wide range of styles, from photorealism and cinematic shots to 3D render, oil painting, and illustration.
- High Resolution: Supports generation of high-definition images suitable for professional use.
Designed For
- Professional Designers: Create high-quality assets, mockups, and final visuals.
- Digital Artists: Explore complex concepts and generate detailed artwork.
- Marketing Agencies: Produce campaign-ready visuals with specific brand requirements.
- Enterprise Users: High-demand use cases requiring consistent, top-tier visual output.
Input Requirements
To achieve the best results, follow these guidelines:
Text Prompt
- Content: Detailed English descriptions of the subject, setting, lighting, style, and mood.
- Length: Supports long context, but concise and descriptive prompts often yield the best focus.
- Negative Prompt: Optional. Specify elements to exclude (e.g., "blur, low quality, distortion").
Parameters
- Aspect Ratio: Supports various standard ratios (1:1, 16:9, 9:16, 4:3, 3:4).
- Resolution: Optimized for high-resolution outputs (e.g., 1024x1024 and above).
- Steps/Guidance: Configurable for fine-tuning the balance between prompt adherence and image quality.
Pricing
Billing is typically based on the number of images generated and the resolution selected.
- Billing Logic: Per-image generation cost.
- Tier: "Max" tier commands a premium rate due to higher computational resources and output quality compared to standard models.
How to Use
- Enter Prompt: Describe the image you want to generate in detail.
- Set Parameters: Choose your desired aspect ratio and number of images.
- Generate: Submit the request to the Qwen-Image Max model.
- Refine: Use the generated image as a reference or adjust the prompt for iterations.
Best Practices
- Be Specific: Instead of "a cat," try "a fluffy white Persian cat sitting on a velvet sofa, cinematic lighting, 8k resolution."
- Define Style: Explicitly state the medium (e.g., "oil painting," "photograph," "3D render").
- Lighting & Composition: Mention lighting conditions (e.g., "golden hour," "studio lighting") and camera angles.
- Iterate: If the first result isn't perfect, tweak the prompt or use a negative prompt to remove unwanted elements.
Limitations
- Text Accuracy: While improved, complex or long text strings within the image may still occasionally have minor errors.
- Spatial Logic: Extremely complex spatial arrangements might sometimes require prompt tuning.
Version
- Model: Alibaba Qwen-Image Text-to-Image Max
- Family: Qwen-Image
- Technical Context: Large-scale diffusion transformer model optimized for maximum visual fidelity.
