Qwen-Image Text-to-Image Max API by Alibaba
alibaba/qwen-image/text-to-image-max
Text-to-image-max
General-purpose image generation model that supports various art styles and is particularly good at rendering complex text.
程式碼範例
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()安裝
為您的程式語言安裝所需的套件。
bash
pip install requests驗證
所有 API 請求都需要透過 API 金鑰進行驗證。您可以從 Atlas Cloud 儀表板取得 API 金鑰。
bash
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP 標頭
python
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}請妥善保管您的 API 金鑰
切勿在客戶端程式碼或公開儲存庫中暴露您的 API 金鑰。請改用環境變數或後端代理。
提交請求
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())提交請求
提交非同步生成請求。API 會傳回一個預測 ID，您可以用它來檢查狀態並取得結果。
POST
/api/v1/model/generateImage請求主體
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']}")回應
{
"id": "pred_abc123",
"status": "processing",
"model": "model-name",
"created_at": "2025-01-01T00:00:00Z"
}檢查狀態
輪詢預測端點以檢查請求的當前狀態。
GET
/api/v1/model/prediction/{prediction_id}輪詢範例
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)狀態值
processing請求仍在處理中。completed生成完成。輸出已可取得。succeeded生成成功。輸出已可取得。failed生成失敗。請檢查錯誤欄位。完成回應
{
"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"
}
}上傳檔案
上傳檔案至 Atlas Cloud 儲存空間並取得 URL，可用於您的 API 請求。使用 multipart/form-data 上傳。
POST
/api/v1/model/uploadMedia上傳範例
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}")回應
{
"data": {
"download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
"file_name": "image.png",
"content_type": "image/png",
"size": 1024000
}
}輸入 Schema
以下參數可在請求主體中使用。
總計: 0必填: 0選填: 0
無可用參數。
範例請求主體
json
{
"model": "alibaba/qwen-image/text-to-image-max"
}輸出 Schema
API 傳回包含生成輸出 URL 的預測回應。
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.
範例回應
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 Skills 將 300 多個 AI 模型直接整合至您的 AI 程式碼助手。一鍵安裝，即可使用自然語言生成圖片、影片，以及與 LLM 對話。
支援的客戶端
Claude Code
OpenAI Codex
Gemini CLI
Cursor
Windsurf
VS Code
Trae
GitHub Copilot
Cline
Roo Code
Amp
Goose
Replit
40+ 支援的客戶端
安裝
bash
npx skills add AtlasCloudAI/atlas-cloud-skills設定 API 金鑰
從 Atlas Cloud 儀表板取得 API 金鑰，並設為環境變數。
bash
export ATLASCLOUD_API_KEY="your-api-key-here"功能
安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。
圖片生成使用 Nano Banana 2、Z-Image 等模型生成圖片。
影片創作使用 Kling、Vidu、Veo 等從文字或圖片創建影片。
LLM 對話與 Qwen、DeepSeek 及其他大型語言模型對話。
媒體上傳上傳本機檔案，用於圖片編輯和圖片轉影片工作流程。
MCP Server
Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 300 多個 AI 模型連接。支援任何 MCP 相容的客戶端。
支援的客戶端
Cursor
VS Code
Windsurf
Claude Code
OpenAI Codex
Gemini CLI
Cline
Roo Code
100+ 支援的客戶端
安裝
bash
npx -y atlascloud-mcp設定
將以下設定新增至您 IDE 的 MCP 設定檔中。
json
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}可用工具
atlas_generate_image根據文字提示生成圖片。
atlas_generate_video從文字或圖片創建影片。
atlas_chat與大型語言模型對話。
atlas_list_models瀏覽 300 多個可用的 AI 模型。
atlas_quick_generate一步完成內容創建，自動選擇模型。
atlas_upload_media上傳本機檔案用於 API 工作流程。
API Schema
Schema 不可用暫無可用範例
Alibaba 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.
