Z-Image Turbo API by Alibaba
Z-Image-Turbo is a 6 billion parameter text-to-image model that generates photorealistic images in sub-second time. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
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": "z-image/turbo",
"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。
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。
export ATLASCLOUD_API_KEY="your-api-key-here"HTTP 標頭
import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}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。
/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": "z-image/turbo",
"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。
/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。
/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。
無可用參數。
範例請求主體
{
"model": "z-image/turbo"
}輸出 Schema
API 傳回包含生成輸出 URL 的預測回應。
範例回應
{
"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
安裝
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。
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。
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
安裝
npx -y atlascloud-mcp設定
將以下設定新增至您 IDE 的 MCP 設定檔中。
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}可用工具
API Schema
Schema 不可用Z-Image Turbo - 極速文生圖模型
最新阿里巴巴通義萬相團隊 60 億參數模型
Z-Image Turbo 是排名第一的開源文生圖模型，在 Artificial Analysis Image Arena 上超越了 FLUX.2 [dev]、HunyuanImage 3.0 和 Qwen-Image。由阿里巴巴通義萬相團隊（獨立於 Qwen/Wan 團隊）打造，這款 60 億參數模型透過先進的 Decoupled-DMD 蒸餾技術實現亞秒級生成，同時保持逼真的圖像品質。僅需 8 個推理步驟，適配 16GB 顯存，為速度關鍵的生產環境提供專業級結果。
- 僅需 8 個推理步驟（競品需 20-50 步）
- H800 GPU 上實現亞秒級生成
- 比 Qwen Image 每步快 1.31-1.41 倍
- 適配 16GB 顯存（RTX 3060/4090）
- AI Arena 開源模型排名第一
- 中英文雙語文本渲染
- 強大的指令遵循能力
- 全方位超越 FLUX.1 [dev] 和 Qwen
阿里巴巴戰略模型矩陣
阿里巴巴提供三大專業 AI 圖像生成系統，各自針對不同應用場景優化
Z-Image Turbo
通義萬相團隊
- ⚡ 最快：8 步推理，亞秒生成
- 🏆 開源模型排名第一
- 💰 最具性價比（$0.005/張）
- 🎯 快速迭代優化
Qwen-Image
通義千問團隊
- 🎨 無與倫比的真實感和皮膚紋理
- 💡 卓越的光照交互效果
- ⏱️ 較慢（20秒 vs Z-Image 的 5-10秒）
- 🎯 適合高端製作工作
Wan 2.5/2.6
通義萬相團隊
- 🎬 文生視頻 + 圖生視頻
- 📹 多解析度支援（480P-720P）
- 🔄 音視頻同步
- 🎯 跨模態內容生成
Key Insight: Z-Image Turbo 比 Qwen-Image 每步快 1.31-1.41 倍，非常適合需要快速生成的應用場景。雖然 Qwen-Image 在最終渲染的真實感方面略勝一籌，但 Z-Image Turbo 在生產環境中提供了速度和品質的最佳平衡。
技術亮點
採用單流擴散 Transformer（S3-DiT）架構，統一處理各種條件輸入。這種 60 億參數設計在不增加大模型計算開銷的情況下實現專業級結果，同時保持最先進的品質。
先進的蒸餾演算法配合 CFG 增強和分佈匹配機制，實現 8 步推理（競品需 20-50 步）。在 H800 GPU 上實現亞秒級生成，在消費級 RTX 3060/4090（16GB 顯存）上流暢運行。
在 Artificial Analysis Image Arena 上排名第一的開源模型，超越 FLUX.2 [dev]、HunyuanImage 3.0 和 Qwen-Image。擅長中英文雙語文本渲染、逼真圖像生成和強大的指令遵循。採用 Apache 2.0 許可證，允許商業使用。
完美適用於
為什麼選擇 Z-Image Turbo
即時生成
亞秒級生成，零冷啟動延遲。立即獲得您的圖像，無需任何等待。高性價比
實惠的價格，每張圖片僅需 $0.005。輕鬆擴展您的創意專案，無需擔心預算。開箱即用的 API
簡單的 REST API 整合。透過我們完善的文檔，幾分鐘內即可開始生成圖像。技術規格
立即開始使用 Z-Image Turbo
體驗極速、逼真的圖像生成。無需設定，呼叫我們的 API 即可開始創作。
Z-Image-Turbo — 6B-parameter, ultra-fast text-to-image
Z-Image-Turbo is a 6B-parameter text-to-image model from Tongyi-MAI, engineered for production workloads where latency and throughput really matter. It uses only 8 sampling steps to render a full image, achieving sub-second latency on data-center GPUs and running comfortably on many 16 GB VRAM consumer cards.
Ultra-fast generation with production-ready quality
Where many diffusion models need dozens of steps, Z-Image-Turbo is aggressively optimised around an 8-step sampler. That keeps inference extremely fast while still delivering photorealistic images and reliable on-image text, making it a strong fit for interactive products, dashboards, and large-scale backends—not just offline batch jobs.
Why it looks so good?
- Photorealistic output at speed Generates high-fidelity, realistic images that work for product photos, hero banners, and UI visuals without multi-second waits.
- Bilingual prompts and text Understands prompts in English and Chinese, and can render multilingual text directly in the image—helpful for cross-market campaigns, posters, and screenshots.
- Low-latency, low-step design Only 8 function evaluations per image deliver extremely low latency, ideal for chatbots, configuration tools, design assistants, and any “click → image” experience.
- Friendly VRAM footprint Runs well in 16 GB VRAM environments, reducing hardware costs and making local or edge deployments more realistic.
- Scales for bulk generation Its efficiency makes large jobs—catalogues, continuous feed images, or auto-generated thumbnails—practical without blowing up compute budgets.
- Reproducible generations A controllable seed parameter lets you recreate a previous image or generate small, controlled variations for brand safety and experimentation.
How to use
- prompt – natural-language description of the scene, style, and any on-image text (English or Chinese).
- size (width / height) – choose the output resolution; supports square and rectangular images up to high resolutions (for example, 1536 × 1536).
- seed – set to -1 for random results, or use a fixed integer to make outputs reproducible.
Pricing
Simple per-image billing:
- Without prompt rewriting (prompt_extend=false): $0.015 per generated image
- With prompt rewriting (prompt_extend=true): $0.03 per generated image
Try more models and see their difference!
- Nano Banana Pro – Text-to-Image – Google’s Nano Banana Pro (Gemini 3.0 Pro Image family) delivers high-quality multi-image generation with extremely low cost per image, ideal for large-scale applications.
- Seedream V4 – Text-to-Image – ByteDance’s high-resolution text-to-image model with rich detail and diverse styles, well suited for creative illustration and commercial visuals.
- FLUX.2 [dev] – Text-to-Image – A lightweight FLUX.2-based base model hosted by AtlasCloud, optimised for efficient inference and LoRA-friendly training.
